The state of the art of deep learning models in medical science and their challenges

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805 · see citing works

A fast learning algorithm for deep belief nets

Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336 · see citing works

Untitled work

(2016) · Cited by 11,679 · see citing works

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works

Building a Large Annotated Corpus of English: The Penn Treebank

Marcus, M. (1993) · doi:10.21236/ada273556 · Cited by 7,405 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000006 · Cited by 7,048 · see citing works

Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning

Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759 · see citing works

Using Deep Learning for Image-Based Plant Disease Detection

Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016) · Frontiers in Plant Science · doi:10.3389/fpls.2016.01419 · Cited by 5,111 · see citing works

Learning Deep Architectures for AI

Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 5,037 · see citing works

Deep learning in medical image analysis

Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

An analysis of single-layer networks in unsupervised feature learning

Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760 · see citing works

Scalable and accurate deep learning with electronic health records

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · see citing works

Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations

Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412 · see citing works

Scalable and accurate deep learning with electronic health records.

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242 · see citing works

End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography

Ardila, D., Kiraly, A. P., Bharadwaj, S., Choi, B., Reicher, J. J., Peng, L., Tse, D., Etemadi, M., Ye, W., Corrado, G., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0447-x · Cited by 2,167 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works

Deep Boltzmann machines

Salakhutdinov, R., & Hinton, G. E. (2009) · Cited by 1,890 · see citing works

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks

Lakhani, P., & Sundaram, B. (2017) · Radiology · doi:10.1148/radiol.2017162326 · Cited by 1,775 · see citing works

Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

Greenspan, H. K., van Ginneken, B., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2553401 · Cited by 1,704 · see citing works

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,658 · see citing works

Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach

Glorot, X., Bordes, A., & Bengio, Y. (2011) · Cited by 1,569 · see citing works

Deep Learning Applications in Medical Image Analysis

Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Razzak, I., Naz, S., & Zaib, A. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,305 · see citing works

Deep Learning in Medical Imaging: General Overview

Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., & Kim, N. (2017) · Korean Journal of Radiology · doi:10.3348/kjr.2017.18.4.570 · Cited by 1,287 · see citing works

Untitled work

(2011) · Cited by 1,258 · see citing works

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D. R. J., Cree, I. A., & Rajpoot, N. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2525803 · Cited by 1,224 · see citing works

Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.938364 · Cited by 1,128 · see citing works

Overview of deep learning in medical imaging

Suzuki, K. (2017) · Radiological Physics and Technology · doi:10.1007/s12194-017-0406-5 · Cited by 1,102 · see citing works

Deep, Big, Simple Neural Nets for Handwritten Digit Recognition

Ciresan, D. C., Meier, U., Gambardella, L. M., & Schmidhuber, J. (2010) · Neural Computation · doi:10.1162/neco_a_00052 · Cited by 1,094 · see citing works

Deep learning-based multi-omics integration robustly predicts survival in liver cancer

Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091 · see citing works

Classification using deep learning neural networks for brain tumors

Mohsen, H., El‐Dahshan, E. A., El-Horbaty, E.-S. M., & Salem, A.-B. M. (2017) · Future Computing and Informatics Journal · doi:10.1016/j.fcij.2017.12.001 · Cited by 1,076 · see citing works

Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis

Litjens, G., Sánchez, C. I., Timofeeva, N., Hermsen, M., Nagtegaal, I., Kovacs, I., Hulsbergen - van de Kaa, C., Bult, P., van Ginneken, B., & van der Laak, J. (2016) · Scientific Reports · doi:10.1038/srep26286 · Cited by 1,069 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012 · see citing works

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968 · see citing works

Large scale deep learning for computer aided detection of mammographic lesions

Kooi, T., Litjens, G., van Ginneken, B., Gubern-Mérida, A., Sánchez, C. I., Mann, R., den Heeten, A., & Karssemeijer, N. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.07.007 · Cited by 965 · see citing works

A novel deep learning based framework for the detection and classification of breast cancer using transfer learning

Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. P. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.03.022 · Cited by 796 · see citing works

Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans

Cheng, J.-Z., Ni, D., Chou, Y.-H., Qin, J., Tiu, C.-M., Chang, Y.-C., Huang, C.-S., Shen, D., & Chen, C.-M. (2016) · Scientific Reports · doi:10.1038/srep24454 · Cited by 755 · see citing works

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701 · see citing works

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T. P., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2495 · Cited by 635 · see citing works

A deep learning approach for Parkinson’s disease diagnosis from EEG signals

Oh, S. L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., & Acharya, U. R. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3689-5 · Cited by 630 · see citing works

Deep Learning and Its Applications in Biomedicine

Cao, C., Liu, F., Tan, H., Song, D., Shu, W., Li, W., Zhou, Y., Bo, X., & Xie, Z. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2017.07.003 · Cited by 625 · see citing works

A time-delay neural network architecture for isolated word recognition

Lang, K. J., Waibel, A., & Hinton, G. E. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90044-l · Cited by 596 · see citing works

Deep Learning in Radiology

McBee, M. P., Awan, O. A. R., Colucci, A. T., Ghobadi, C. W., Kadom, N., Kansagra, A. P., Tridandapani, S., & Auffermann, W. F. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.02.018 · Cited by 502 · see citing works

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works

Automated pulmonary nodule detection in CT images using deep convolutional neural networks

Xie, H., Yang, D., Sun, N., Chen, Z., & Zhang, Y. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.07.031 · Cited by 464 · see citing works

Deep Learning-Based Feature Representation for AD/MCI Classification

Suk, H., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_72 · Cited by 463 · see citing works

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP

Yu, D., & Deng, L. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939038 · Cited by 459 · see citing works

Unsupervised Feature Learning and Deep Learning: A Review and New Perspectives

Bengio, Y., Courville, A. C., & Vincent, P. (2012) · Cited by 455 · see citing works

Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring

Kallenberg, M., Petersen, K., Nielsen, M., Ng, A. Y., Diao, P., Igel, C., Vachon, C. M., Holland, K., Winkel, R. R., Karssemeijer, N., et al. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2532122 · Cited by 439 · see citing works

Medical image classification using synergic deep learning

Zhang, J., Xie, Y., Wu, Q., & Xia, Y. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.010 · Cited by 398 · see citing works

AI in Health: State of the Art, Challenges, and Future Directions

Wang, F., & Preininger, A. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677908 · Cited by 398 · see citing works

Chest pathology detection using deep learning with non-medical training

Bar, Y., Diamant, I., Wolf, L., Lieberman, S., Konen, E., & Greenspan, H. (2015) · 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2015.7163871 · Cited by 392 · see citing works

Deep learning of feature representation with multiple instance learning for medical image analysis

Xu, Y. L., Mo, T., Feng, Q., Zhong, P., Lai, M., & Chang, E. (2014) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2014.6853873 · Cited by 385 · see citing works

Deep learning for lung Cancer detection and classification

Asuntha, A., & Srinivasan, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08394-3 · Cited by 383 · see citing works

Randomized controlled trial of a 12-week digital care program in improving low back pain

Shebib, R., Bailey, J. F., Smittenaar, P., Perez, D. A., Mecklenburg, G., & Hunter, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-018-0076-7 · Cited by 341 · see citing works

Classification of acute lymphoblastic leukemia using deep learning

Rehman, A. U., Abbas, N., Saba, T., Rahman, S. I. U., Mehmood, Z., & Kolivand, H. (2018) · Microscopy Research and Technique · doi:10.1002/jemt.23139 · Cited by 333 · see citing works

Large-scale Learning with SVM and Convolutional for Generic Object Categorization

Huang, F. J., & LeCun, Y. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.164 · Cited by 320 · see citing works

Deep Machine Learning—A New Frontier in Artificial Intelligence Research

Arel, I., Rose, D. C., & Karnowski, T. P. (2010) · Cited by 314 · see citing works

Skin Lesion Classification Using Hybrid Deep Neural Networks

Mahbod, A., Schaefer, G., Wang, C., Ecker, R., & Ellinger, I. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8683352 · Cited by 304 · see citing works

Phoneme Recognition Using Time-Delay Neural Networks

Waibel, A. H., Hanazawa, T., Hinton, G. E., Shikano, K., & LANG, K. J. (1990) · Elsevier eBooks · doi:10.1016/b978-0-08-051584-7.50037-1 · Cited by 301 · see citing works

Deep CNNs for microscopic image classification by exploiting transfer learning and feature concatenation

Nguyen, L. D., Lin, D., Lin, Z., & Cao, J. (2018) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas.2018.8351550 · Cited by 293 · see citing works

Discrimination of Breast Cancer with Microcalcifications on Mammography by Deep Learning

Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., & Li, L. (2016) · Scientific Reports · doi:10.1038/srep27327 · Cited by 291 · see citing works

A deep learning method for classifying mammographic breast density categories

Mohamed, A. A., Berg, W. A., Peng, H., Luo, Y., Jankowitz, R. C., & Wu, S. (2017) · Medical Physics · doi:10.1002/mp.12683 · Cited by 266 · see citing works

Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

Hossain, M. S., Muhammad, G., & Alamri, A. M. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0561-x · Cited by 246 · see citing works

Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

Dhungel, N., Carneiro, G., & Bradley, A. P. (2015) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2015.7371234 · Cited by 233 · see citing works

Deep learning for infrared thermal image based machine health monitoring

Janssens, O., Van de Walle, R., Loccufier, M., & Van Hoecke, S. (2018) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2722479 · Cited by 207 · see citing works

BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights

Benhammou, Y., Achchab, B., Herrera, F., & Tabik, S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2019.09.044 · Cited by 170 · see citing works

A concept space approach to addressing the vocabulary problem in scientific information retrieval: An experiment on the Worm Community System

Chen, H., Ng, T. D., Martinez, J., & Schatz, B. R. (1997) · Journal of the American Society for Information Science · doi:10.1002/(sici)1097-4571(199701)48:1 · Cited by 159 · see citing works

Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation

Liao, S., Gao, Y., Oto, A., & Shen, D. G. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_32 · Cited by 157 · see citing works

Convolutional neural networks for mammography mass lesion classification

Arévalo, J., Gonzalez, F. A., Ramos-Pollán, R., Oliveira, J. L., & Guevara, M. A. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318482 · Cited by 147 · see citing works

Diagnosis of Focal Liver Diseases Based on Deep Learning Technique for Ultrasound Images

Hassan, T. M., Elmogy, M. M., & Sallam, E. A. (2017) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-016-2387-9 · Cited by 123 · see citing works

A deep learning approach to machine transliteration

Thomas Deselaers, B., Hasan, S., Bender, O., & Ney, H. (2009) · Conference on Machine Translation (WMT) · doi:10.3115/1626431.1626476 · Cited by 120 · see citing works

Face Detection Using Convolutional Neural Networks and Gabor Filters

Kwolek, B. (2005) · Lecture notes in computer science · doi:10.1007/11550822_86 · Cited by 114 · see citing works

A completely automated CAD system for mass detection in a large mammographic database

Bellotti, R., Carlo, F. D., Tangaro, S., Gargano, G., Maggipinto, G., Castellano, M., Massafra, R., Cascio, D., Fauci, F., Magro, R., et al. (2006) · Medical Physics · doi:10.1118/1.2214177 · Cited by 112 · see citing works

Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey

Deng, L. (2012) · APSIPA Transactions on Signal and Information Processing · Cited by 110 · see citing works

Deep belief net learning in a long-range vision system for autonomous off-road driving

Hadsell, R., Erkan, A., Sermanet, P., Scoffier, M., Muller, U., & LeCun, Y. (2008) · 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2008.4651217 · Cited by 101 · see citing works

Pixel-Based Machine Learning in Medical Imaging

Suzuki, K. (2012) · International Journal of Biomedical Imaging · doi:10.1155/2012/792079 · Cited by 93 · see citing works

Deep Learning: A ReviewPreprint

Vargas, R. H., Mosavi, A. H., & Ruiz, R. (2018) · Preprints.org · doi:10.20944/preprints201810.0218.v1 · Cited by 93 · see citing works

Segmentation of blood vessels using rule-based and machine-learning-based methods: a review

Zhao, F., Chen, Y., Hou, Y., & He, X. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0580-7 · Cited by 82 · see citing works

The Application of a Convolution Neural Network on Face and License Plate Detection

Chen, Y.-N., Han, C., Wang, C.-T., Jeng, B.-S., & Fan, K. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.1115 · Cited by 80 · see citing works

Deep belief networks and cortical algorithms: A comparative study for supervised classification

Rizk, Y., Hajj, N., Mitri, N., & Awad, M. (2018) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.01.004 · Cited by 71 · see citing works

Medical images fusion by using weighted least squares filter and sparse representation

Jiang, W., Yang, X., Wu, W., Liu, K., Ahmad, A., Sangaiah, A. K., & Jeon, G. (2018) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2018.03.037 · Cited by 54 · see citing works

Mariana: tencent deep learning platform and its applications

Zou, Y., Jin, X., Li, Y., Guo, Z., Wang, E., & Xiao, B. (2014) · Proceedings of the VLDB Endowment · doi:10.14778/2733004.2733082 · Cited by 49 · see citing works

A classification framework for prediction of breast density using an ensemble of neural network classifiers

Kumar, I., Bhadauria, H. S., Virmani, J., & Thakur, S. (2017) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2017.01.001 · Cited by 46 · see citing works

Convolutional networks for speech detection

Sukittanon, S., Surendran, A. C., Platt, J. C., & Burges, C. J. C. (2004) · Interspeech (USB) · doi:10.21437/interspeech.2004-376 · Cited by 45 · see citing works

Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms

Rajpurohit, S., Patil, S., Choudhary, N., Gavasane, S., & Kosamkar, P. K. (2018) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2018.8554576 · Cited by 44 · see citing works

Unsupervised and transfer learning challenge

Guyon, I., Dror, G., Lemaire, V., Taylor, G. W., & Aha, D. W. (2011) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2011.6033302 · Cited by 37 · see citing works

Mammogram classification using dynamic time warping

Gardezi, S. J. S., Faye, I., Sánchez‐Bornot, J. M., Kamel, N., & Hussain, M. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4328-8 · Cited by 36 · see citing works

A novel automatic image segmentation method for Chinese literati paintings using multi-view fuzzy clustering technology

Zhang, J., Zhou, Y., Xia, K., Jiang, Y., & Liu, Y. (2019) · Multimedia Systems · doi:10.1007/s00530-019-00627-7 · Cited by 35 · see citing works

Automated segmentation of choroidal neovascularization in optical coherence tomography images using multi-scale convolutional neural networks with structure prior

Xi, X., Meng, X., Yang, L., Nie, X., Yang, G., Chen, H., Fan, X., Yin, Y., & Chen, X. (2018) · Multimedia Systems · doi:10.1007/s00530-017-0582-5 · Cited by 31 · see citing works

Oscillometric blood pressure estimation by combining nonparametric bootstrap with Gaussian mixture model

Lee, S., Rajan, S., Jeon, G., Chang, J., Dajani, H. R., & Groza, V. Z. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.11.008 · Cited by 30 · see citing works

Deep learning-based automatic downbeat tracking: a brief review

Jia, B., Lv, J., & Liu, D. (2019) · Multimedia Systems · doi:10.1007/s00530-019-00607-x · Cited by 29 · see citing works

A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms

Kumar, I., Bhadauria, H. S., Virmani, J., & Thakur, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4340-z · Cited by 24 · see citing works

Exploration of an innovative geometric parameter based on performance enhancement for foot print recognition

Kumar, V. D. A., Malathi, S., Venkatesan, R., Ramalakshmi, K., Vengatesan, K., Ding, W., & Kumar, A. (2019) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-190982 · Cited by 22 · see citing works

Improving resolution of medical images with deep dense convolutional neural network

Wei, S., Wu, W., Jeon, G., Ahmad, A., & Yang, X. (2018) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.5084 · Cited by 21 · see citing works

Acoustic modeling using deep belief network for Bangla speech recognition

Ahmed, M., Shill, P. C., Islam, K., Mollah, M. A. S., & Akhand, M. A. H. (2015) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccitechn.2015.7488087 · Cited by 18 · see citing works

Mammogram Classification Schemes by Using Convolutional Neural Networks

Taboada, D. O. S., Aguilar, C., Ramírez-Morales, I., Tusa, E., Asanza, W. B. R., & PINTA, M. A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-72727-1_6 · Cited by 15 · see citing works

An adaptive anchored neighborhood regression method for medical image enhancement

Jiang, L., Ye, S., Yang, X., Ma, X., Lu, L., Ahmad, A., & Jeon, G. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08353-y · Cited by 9 · see citing works

Patch-wise label propagation for MR brain segmentation based on multi-atlas images

Wang, Y., Zu, C., Ma, Z., Luo, Y., He, K., Wu, X., & Zhou, J. (2017) · Multimedia Systems · doi:10.1007/s00530-017-0577-2 · Cited by 8 · see citing works

A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors

Kumar, I., Virmani, J., & Bhadauria, H. S. (2018) · International Journal of Computational Systems Engineering · doi:10.1504/ijcsyse.2018.10012637 · Cited by 3 · see citing works

A computerised framework for prediction of fatty and dense breast tissue using principal component analysis and multi-resolution texture descriptors

Kumar, I., Bhadauria, H. S., & Virmani, J. (2018) · International Journal of Computational Systems Engineering · doi:10.1504/ijcsyse.2018.091386 · Cited by 3 · see citing works

On Improving Breast Density Segmentation Using Conditional Generative Adversarial Networks

Saffari, N., Rashwan, H. A., Herrera, B., Romaní, S., Meritxell, A., & nec, P. D. E. (2018) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-918-8-386 · Cited by 2 · see citing works

Evaluation of artificial intelligence techniques in disease diagnosis and prediction

Nia, N. G., Kaplanoğlu, E., & Nasab, A. (2023) · Discover Artificial Intelligence · doi:10.1007/s44163-023-00049-5 · Cited by 462 · see citing works

Machine learning and deep learning approach for medical image analysis: diagnosis to detection

Rana, M., & Bhushan, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14305-w · Cited by 367 · see citing works

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches

Xu, Y., Quan, R., Xu, W., Huang, Y., Chen, X., & Liu, F. (2024) · Bioengineering · doi:10.3390/bioengineering11101034 · Cited by 350 · see citing works

Opportunities of Artificial Intelligence and Machine Learning in the Food Industry

Kumar, I., Rawat, J., Mohd, N., & Husain, S. (2021) · Journal of Food Quality · doi:10.1155/2021/4535567 · Cited by 302 · see citing works

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Nasser, M., & Yusof, U. K. (2023) · Diagnostics · doi:10.3390/diagnostics13010161 · Cited by 227 · see citing works

DL-CNN-based approach with image processing techniques for diagnosis of retinal diseases

Tayal, A., Gupta, J., Solanki, A., Bisht, K., Nayyar, A., & Masud, M. (2021) · Multimedia Systems · doi:10.1007/s00530-021-00769-7 · Cited by 140 · see citing works

Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Pouyanfar, N., Harofte, S. Z., Soltani, M., Siavashy, S., Asadian, E., Ghorbani‐Bidkorbeh, F., Keçili, R., & Hussain, C. M. (2022) · Trends in Environmental Analytical Chemistry · doi:10.1016/j.teac.2022.e00160 · Cited by 131 · see citing works

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2023) · Sensors · doi:10.3390/s23020828 · Cited by 120 · see citing works

Advanced Mango Leaf Disease Detection and Severity Analysis with Federated Learning and CNN

Mehta, S., Kukreja, V., & Yadav, R. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205922 · Cited by 110 · see citing works

A comprehensive review of community detection in graphs

Li, J., Lai, S., Shuai, Z., Tan, Y., Jia, Y., Yu, M., Song, Z., Peng, X., Xu, Z., Ni, Y., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128169 · Cited by 116 · see citing works

Breast cancer classification based on hybrid CNN with LSTM model

Kaddes, M., Ayid, Y. M., Elshewey, A. M., & Fouad, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-88459-6 · Cited by 98 · see citing works

Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment

Harofte, S. Z., Soltani, M., Siavashy, S., & Raahemifar, K. (2022) · Small · doi:10.1002/smll.202203169 · Cited by 92 · see citing works

High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications

Zhou, J., Dong, J., Hou, H., Huang, L., & Li, J. (2024) · Lab on a Chip · doi:10.1039/d3lc01012k · Cited by 92 · see citing works

Lung cancer medical images classification using hybrid CNN-SVM

Saleh, A. Y., Chin, C. K., Penshie, V., & Al-Absi, H. R. H. (2021) · International Journal of Advances in Intelligent Informatics · doi:10.26555/ijain.v7i2.317 · Cited by 74 · see citing works

Review of applications of deep learning in veterinary diagnostics and animal health

Xiao, S., Dhand, N. K., Wang, Z., Hu, K., Thomson, P. C., House, J. K., & Khatkar, M. S. (2025) · Frontiers in Veterinary Science · doi:10.3389/fvets.2025.1511522 · Cited by 82 · see citing works

Dense Tissue Pattern Characterization Using Deep Neural Network

Kumar, I., Kumar, A., Kumar, V. D. A., Kannan, R., Vimal, V., Singh, K. U., & Mahmud, M. (2022) · Cognitive Computation · doi:10.1007/s12559-021-09970-2 · Cited by 64 · see citing works

Exploring the current and prospective role of artificial intelligence in disease diagnosis

Aamir, A., Iqbal, A., Jawed, F., Ashfaque, F., Hafsa, H., Anas, Z., Oduoye, M. O., Basit, A., Ahmed, S., Rauf, S. A., et al. (2024) · Annals of Medicine and Surgery · doi:10.1097/ms9.0000000000001700 · Cited by 71 · see citing works

Smart Wearables for the Detection of Occupational Physical Fatigue: A Literature Review

Moshawrab, M., Adda, M., Bouzouane, A., Ibrahim, H., & Raad, A. (2022) · Sensors · doi:10.3390/s22197472 · Cited by 62 · see citing works

Scientometric Analysis and Classification of Research Using Convolutional Neural Networks: A Case Study in Data Science and Analytics

Daradkeh, M. K., Abualigah, L., Atalla, S., & Mansoor, W. (2022) · Electronics · doi:10.3390/electronics11132066 · Cited by 60 · see citing works

A Study on Cervical Cancer Prediction using Various Machine Learning Approaches

Rawat, P., Bajaj, M., Mehta, S., Sharma, V., & Vats, S. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099493 · Cited by 58 · see citing works

Role of artificial intelligence in early diagnosis and treatment of infectious diseases

Srivastava, V., Kumar, R., Wani, M. Y., Robinson, K. M., & Ahmad, A. (2024) · Scandinavian Journal of Infectious Diseases · doi:10.1080/23744235.2024.2425712 · Cited by 61 · see citing works

A Survey on Medical Image Segmentation Based on Deep Learning Techniques

Moorthy, J., & Gandhi, U. D. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040117 · Cited by 50 · see citing works

A Comprehensive Analysis of the Effectiveness of Machine Learning Algorithms for Predicting Water Quality

Rawat, P., Bajaj, M., Sharma, V., & Vats, S. (2023) · International Conference on Innovative Data Communication Technologies and Application (ICIDCA) · doi:10.1109/icidca56705.2023.10099968 · Cited by 50 · see citing works

A Comprehensive Review of Advanced Deep Learning Approaches for Food Freshness Detection

Singh, R., Nickhil, C., R.Nisha, Upendar, K., Bhukya, J., & Deka, S. C. (2024) · Food Engineering Reviews · doi:10.1007/s12393-024-09385-3 · Cited by 47 · see citing works

Attention-based end-to-end CNN framework for content-based X-ray image retrieval

Öztürk, Ş., Alhudhaif, A., & Polat, K. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2105-242 · Cited by 42 · see citing works

An Efficient and Light Weight Deep Learning Model for Accurate Retinal Vessels Segmentation

Aurangzeb, K., Alharthi, R. S., Haider, S. I., & Alhussein, M. A. (2022) · IEEE Access · doi:10.1109/access.2022.3217782 · Cited by 39 · see citing works

Deep learning for personalized health monitoring and prediction: A review

Damaševičius, R., Jagatheesaperumal, S. K., Rajesh, K. N. V. P. S., Hussain, S., Alizadehsani, R., & Górriz, J. M. (2024) · Computational Intelligence · doi:10.1111/coin.12682 · Cited by 37 · see citing works

A Comprehensive Overview and Analysis of Large Language Models: Trends and Challenges

Mohammed, A., & Kora, R. (2025) · IEEE Access · doi:10.1109/access.2025.3573955 · Cited by 38 · see citing works

Transfer learning for histopathology images: an empirical study

Aitazaz, T., Tubaishat, A., Al-Obeidat, F. N., Shah, B., Zia, T., & Tariq, S. A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07516-7 · Cited by 36 · see citing works

Overview of the future impact of wearables and artificial intelligence in healthcare workflows and technology

LaBoone, P. A., & Marques, O. (2024) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2024.100294 · Cited by 57 · see citing works

Lung Infection and Identification using Heatmap

Srivastava, S., Dhyani, N., Sharma, V., Vats, S., Yadav, S. P., Kukreja, V., & Singh, R. (2023) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic56838.2023.10140204 · Cited by 32 · see citing works

Automatic damaged vehicle estimator using enhanced deep learning algorithm

Qaddour, J., & Siddiqa, S. A. (2023) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2023.200192 · Cited by 31 · see citing works

A zero-knowledge proof federated learning on DLT for healthcare data

Petrosino, L., Masi, L., D’Antoni, F., Merone, M., & Vollero, L. (2024) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2024.104992 · Cited by 30 · see citing works

A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks

Kaushal, C., Islam, M. K., Althubiti, S. A., Alenezi, F. S., & Mansour, R. F. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7935346 · Cited by 30 · see citing works

Big Data Analytics in Bioinformatics and Healthcare

Verma, R. K., Kishor, K., & Jha, S. K. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-2426-4.ch002 · Cited by 27 · see citing works

Prediction of genetic alterations from gastric cancer histopathology images using a fully automated deep learning approach

Jang, H., Lee, A., Kang, J. K., Song, I. H., & Lee, S. H. (2021) · World Journal of Gastroenterology · doi:10.3748/wjg.v27.i44.7687 · Cited by 29 · see citing works

Attentive Bi-LSTM-Based Method for Noise Suppression in Ambulatory ECG Measurements

Kumar, V., & Muduli, P. R. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3330182 · Cited by 26 · see citing works

Deep Learning-Empowered Clinical Big Data Analytics in Healthcare Digital Twins

Lv, Z., Guo, J., & Lv, H. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3252668 · Cited by 25 · see citing works

A Systematic Review of Modern Approaches in Healthcare Systems for Lung Cancer Detection and Classification

Pandey, S. K., & Bhandari, A. K. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09940-x · Cited by 24 · see citing works

A hybrid software-defined networking approach for enhancing IoT cybersecurity with deep learning and blockchain in smart cities

Alotaibi, J. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-01935-8 · Cited by 23 · see citing works

YOLOv8 framework for COVID-19 and pneumonia detection using synthetic image augmentation

Uddin, A. H., Abu, R. M., Yang, J., Bhatti, U. A., Ku, C. S., & Por, L. Y. (2025) · Digital Health · doi:10.1177/20552076251341092 · Cited by 23 · see citing works

Segmentation and classification of white blood SMEAR images using modified CNN architecture

Kumar, I., & Rawat, J. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06139-y · Cited by 21 · see citing works

A Survey on Human Cancer Categorization Based on Deep Learning

Ibrahim, A., Mohamed, H. K., Maher, A., & Zhang, B. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.884749 · Cited by 21 · see citing works

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis

Syed, A. H., & Khan, T. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.854927 · Cited by 23 · see citing works

Predicting Breast Cancer An Evaluation of Machine Learning Approaches

Mehta, S., Rawat, P., Bajaj, M., Vats, S., Sharma, V., & Kukreja, V. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205711 · Cited by 19 · see citing works

MSegNet: A Multi-View Coupled Cross-Modal Attention Model for Enhanced MRI Brain Tumor Segmentation

Wang, Y., Xu, J., Guan, Y., Ahmad, F., Mahmood, T., & Rehman, A. U. (2025) · International Journal of Computational Intelligence Systems · doi:10.1007/s44196-025-00787-7 · Cited by 19 · see citing works

Prediction of Hypertension Outcomes Based on Gain Sequence Forward Tabu Search Feature Selection and XGBoost

Chang, W., Ji, X., Xiao, Y., Zhang, Y., Chen, B., Liu, H., & Zhou, S. (2021) · Diagnostics · doi:10.3390/diagnostics11050792 · Cited by 19 · see citing works

Attention-guided neural network for early dementia detection using MRS data

kherchouche, A., Ahmed, O. B., Guillevin, C., Tremblais, B., Julian, A., Fernández-Maloigne, C., & Guillevin, R. (2022) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2022.102074 · Cited by 18 · see citing works

Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments

Alves, C. L., Cury, R. G., Roster, K. I. O., Pineda, A. M., Rodrigues, F. A., Thielemann, C., & Ciba, M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277257 · Cited by 17 · see citing works

Human attention detection system using deep learning and brain–computer interface

Nair, S. A. L., & Megalingam, R. K. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09628-8 · Cited by 17 · see citing works

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis

Hajikarimloo, B., Habibi, M. A., Alvani, M. S., Meinagh, S. O., Kooshki, A., Afkhami-Ardakani, O., Rasouli, F., Tos, S. M., Tavanaei, R., Akhlaghpasand, M., et al. (2024) · Neurological Sciences · doi:10.1007/s10072-024-07879-w · Cited by 17 · see citing works

Machine learning in bioinformatics

Kumar, I., Singh, S. P., & Shivam, S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-323-89775-4.00020-1 · Cited by 16 · see citing works

Investigation of VLLC-OCDM System for Intelligent Transport Applications

Arya, V., Kumari, M., Sharma, N. V., & Singh, P. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270601 · Cited by 15 · see citing works

A Stack Ensemble Approach for Early Alzheimer Classification Using Machine Learning Algorithms

Kumar, A., Sharma, N. V., Chauhan, R. S., Khare, A., Anand, A., & Sharma, M. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-53085-2_8 · Cited by 14 · see citing works

A Comparison of Deep Learning and Traditional Machine Learning Approaches in Detecting Cognitive Impairment Using MRI Scans

Liu, W., Zhang, J., & Zhao, Y. (2022) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac54236.2022.00154 · Cited by 13 · see citing works

Federated learning and differential privacy in clinical health: Extensive survey

Odera, D. (2023) · World Journal of Advanced Engineering Technology and Sciences · doi:10.30574/wjaets.2023.8.2.0113 · Cited by 13 · see citing works

Core Needle Biopsy Guidance Based on Tissue Morphology Assessment with AI-OCT Imaging

Maguluri, G. N., Grimble, J. P., Caron, A., Zhu, G., Krishnamurthy, S., McWatters, A., Beamer, G., Lee, S., & Iftimia, N. V. (2023) · Diagnostics · doi:10.3390/diagnostics13132276 · Cited by 13 · see citing works

A Computational Intelligence Framework Integrating Data Augmentation and Meta-Heuristic Optimization Algorithms for Enhanced Hybrid Nanofluid Density Prediction Through Machine and Deep Learning Paradigms

Mathur, P., Shaikh, H., Sheth, F., Kumar, D., & Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, A. K. (2025) · IEEE Access · doi:10.1109/access.2025.3543475 · Cited by 13 · see citing works

Review on Deep Learning Methodologies in Medical Image Restoration and Segmentation

Christinal, H. A., Hephzibah, R., Kowsalya, G., Jayanthi, R., & Chandy, D. A. (2022) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405618666220407112825 · Cited by 12 · see citing works

Deep clustering framework review using multicriteria evaluation

Ros, F., Riad, R., & Serge Guillaume, A. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111315 · Cited by 12 · see citing works

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches

Zhu, Z. (2024) · Respiratory Medicine · doi:10.1016/j.rmed.2024.107809 · Cited by 12 · see citing works

Machine Learning Approach to Predict Asthma Prevalence with Decision Trees

Mahammad, A. B., & Kumar, R. (2022) · International Conference on Technological Advancements in Computational Sciences (ICTACS) · doi:10.1109/ictacs56270.2022.9988210 · Cited by 12 · see citing works

Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Das, R., Bose, S., Chowdhury, R. S., & Maulik, U. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107182 · Cited by 12 · see citing works

Deep learning in nuclear medicine: from imaging to therapy

Zhang, M.-X., Liu, P., Zhang, M.-D., Su, P., Shang, H.-S., Zhu, J., Wang, D., Ji, X., & Liao, Q.-M. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02031-w · Cited by 11 · see citing works

Deep Learning Techniques in the Cancer-Related Medical Domain: A Transfer Deep Learning Ensemble Model for Lung Cancer Prediction

Jassim, O. A., Abed, M. J., & Saied, Z. H. S. (2023) · Baghdad Science Journal · doi:10.21123/bsj.2023.8340 · Cited by 11 · see citing works

Recapitulating the Cancer‐Immunity Cycle on a Chip

Lee, Y., Min, J., Kim, S., Park, W., Ko, J., & Jeon, N. L. (2024) · Advanced Healthcare Materials · doi:10.1002/adhm.202401927 · Cited by 10 · see citing works

Accurate bladder cancer diagnosis using ensemble deep leaning

El-Atier, R. A., Saraya, M. S., Saleh, A. I., & Rabie, A. H. (2025) · Scientific Reports · doi:10.1038/s41598-025-95002-0 · Cited by 10 · see citing works

Abnormal behavior identification of enterprise cloud platform financial system based on artificial neural network

Wang, Y. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109110 · Cited by 10 · see citing works

Secure Optical Wireless Satellite Network based OWC-OCDM System with DDW code

Arya, V., Kumari, M., Chauhan, R. S., & Sharma, N. V. (2023) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat59970.2023.10353493 · Cited by 9 · see citing works

Machine Learning Techniques for Data-Driven Computer-Aided Diagnostic Method of Polycystic Ovary Syndrome (PCOS) resulting from Functional Ovarian Hyperandrogenism (FOH)

Batra, H., Saluja, K., Gupta, S., Kaushal, R. K., Sharma, N. V., & Singh, P. (2023) · International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES) · doi:10.1109/cises58720.2023.10183485 · Cited by 9 · see citing works

Deep Multi-Scale U-Net Architecture and Label-Noise Robust Training Strategies for Histopathological Image Segmentation

Kurian, N. C., Lehan, A., Verghese, G. E., Dharamshi, N., Meena, S., Li, M., Liu, F., Gillet, C., Rane, S. U., Grigoriadis, A. E., et al. (2022) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe55377.2022.00027 · Cited by 9 · see citing works

Convolutional neural network–multi-kernel radial basis function neural network–salp swarm algorithm: a new machine learning model for predicting effluent quality parameters

Khozani, Z. S., Ehteram, M., Mohtar, W. H. M. W., Achite, M., & Chau, K. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-29406-8 · Cited by 8 · see citing works

Artificial intelligence for digital healthcare in the low and medium income countries

Sibiya, S. S., Hurchund, R., Omondi, B. O., & Owira, P. M. O. (2025) · Health and Technology · doi:10.1007/s12553-025-00950-2 · Cited by 8 · see citing works

Empowering photodynamic therapy with artificial intelligence: current trends and future directions

Paul, A., Xavierselvan, M., Aebisher, D., Kubrak, T. P., Bartusik‐Aebisher, D., & Mallidi, S. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1771804 · Cited by 8 · see citing works

MULTIPLE DISEASE PREDICTION SYSTEM USING MACHINE LEARNING

(2023) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets35252 · Cited by 8 · see citing works

A hybrid deep learning model in predicting weather temperature

Yasavoli, B., Habibirad, A., & Javanshiri, Z. (2025) · Earth Science Informatics · doi:10.1007/s12145-025-01963-1 · Cited by 7 · see citing works

Deep Learning‐Based Segmentation and Risk Stratification for Gastrointestinal Stromal Tumors in Transabdominal Ultrasound Imaging

Zhuo, M., Chen, X., Guo, J., Qian, Q., Xue, E., & Chen, Z. (2024) · Journal of Ultrasound in Medicine · doi:10.1002/jum.16489 · Cited by 7 · see citing works

Technology at the Table: Incorporating AI into Contemporary Food Industry Operations

Kataria, P. (2024) · Food Science and Nutrition Cases · doi:10.1079/foodsciencecases.2024.0003 · Cited by 7 · see citing works

Social robot assisted music course based on speech sensing and deep learning algorithms

Xiao, D. (2024) · Entertainment Computing · doi:10.1016/j.entcom.2024.100814 · Cited by 7 · see citing works

Hybrid Approach to Train Delay Prediction: An Integration of Analytical Model and Deep Learning Techniques

Wu, X., Lian, W., Zhou, M., Dong, H., & Fang, F. (2024) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2024.3440505 · Cited by 7 · see citing works

CBAM Attention Gate‐Based Lightweight Deep Neural Network Model for Improved Retinal Vessel Segmentation

Fareed, K., Khan, A. A., Alhussein, M. A., Aurangzeb, K., Shahzad, A., & Islam, M. (2025) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.70031 · Cited by 6 · see citing works

Intelligent detection and grading diagnosis of fresh rib fractures based on deep learning

Li, T., Liao, M., Fu, Y. M., Zhang, F., Shen, L., Che, J., Wu, S., Liu, J., Wu, W., He, P., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01641-0 · Cited by 6 · see citing works

Recent Challenges in Medical Science using Machine Learning Techniques: A Review

Shah, M., Padhiyar, P., Parmar, K., Patel, J. A., Panesar, S. F., & Raja, K. (2023) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac58438.2023.10290424 · Cited by 6 · see citing works

The analysis of sculpture image classification in utilization of 3D reconstruction under K-means++

Wang, X. (2025) · Scientific Reports · doi:10.1038/s41598-025-01949-5 · Cited by 6 · see citing works

Design of Semiautomatic Digital Creation System for Electronic Music Based on Recurrent Neural Network

Duan, Y., & Wang, J. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/5457376 · Cited by 6 · see citing works

La salud en la era digital

Gutiérrez, C., & López, M. N. (2022) · Revista Médica Clínica Las Condes · doi:10.1016/j.rmclc.2022.11.001 · Cited by 6 · see citing works

Deep Learning Research Directions in Medical Imaging

Simionescu, C., & Iftene, A. (2022) · Mathematics · doi:10.3390/math10234472 · Cited by 6 · see citing works

Evaluating the transferability of machine-learned force fields for material property modeling

Mohanty, S., Yoo, S., Kang, K., & Wei Cai, A. (2023) · Computer Physics Communications · doi:10.1016/j.cpc.2023.108723 · Cited by 6 · see citing works

A Review on the Impacts of Artificial Intelligence (AI) on Youth

Mukhopadhyay, S., & Chakrabarti, A. (2023) · Springer eBooks · doi:10.1007/978-981-99-4969-4_11 · Cited by 6 · see citing works

Imaging update in spondyloarthropathy

Shah, A., Raja, N. R., & Rennie, W. J. (2021) · Journal of Clinical Orthopaedics and Trauma · doi:10.1016/j.jcot.2021.101564 · Cited by 6 · see citing works

Deep learning-based sex estimation of 3D hyoid bone models in a Croatian population using adapted PointNet++ network

Jerković, I., Bašić, Ž., & Kružić, I. (2025) · Scientific Reports · doi:10.1038/s41598-025-07608-z · Cited by 5 · see citing works

A Survey of Large-Scale Deep Learning Models in Medicine and Healthcare

Chen, Z., Liu, R., Huang, S., Guo, Y., & Ren, Y. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.067809 · Cited by 6 · see citing works

Self-supervised learning for breast cancer detection: A review

Figueiras, H., Domingues, J., Matela, N., & Garcia, N. C. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.111245 · Cited by 5 · see citing works

On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments

Alves, C. L., Ciba, M., Toutain, T. G. L. D. O., Porto, J. A. M., de Sena, E. P., Thielemann, C., & Rodrigues, F. A. (2024) · Journal of Physics Complexity · doi:10.1088/2632-072x/ad1c68 · Cited by 4 · see citing works

Study on Degenerative Parkinson's Disease Using Various Machine Learning Algorithms

Bajaj, M., Rawat, P., Sharma, V., Vats, S., Yadav, S. P., & Kukreja, V. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270216 · Cited by 5 · see citing works

A Comparative Review of GPT-4’s Applications in Medicine and High Decision Making

Bitri, R., & Ali, M. (2023) · International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA) · doi:10.1109/contesa61248.2023.10384948 · Cited by 5 · see citing works

Precision Identification of Locally Advanced Rectal Cancer in Denoised CT Scans Using EfficientNet and Voting System Algorithms

Lin, C.-Y., Wu, J. C., Kuan, Y.-M., Liu, Y., Chang, P.-Y., Chen, J. P., Lu, H. H., & Lee, O. K. (2024) · Bioengineering · doi:10.3390/bioengineering11040399 · Cited by 5 · see citing works

Tools for AI-driven Development of Research Competencies

Valbuena, C. N. A. (2023) · LatIA · doi:10.62486/latia202316 · Cited by 5 · see citing works

Pap Smear Medical Image Classification Using Deep Learning: A Systematic Review

Selvano, E., Elwirehardja, G. N., & Pardamean, B. (2023) · International Conference of Computer and Informatics Engineering (IC2IE) · doi:10.1109/ic2ie60547.2023.10331248 · Cited by 5 · see citing works

Open Access Datasets for Active Magnetic Bearing High-Speed Machinery

Rayhan, M., Abubakar, I., Ranjan, G., Sopanen, J., Lindh, T., & Nevaranta, N. (2025) · IEEE Access · doi:10.1109/access.2025.3586026 · Cited by 4 · see citing works

An overview of deep learning in medical imagingPreprint

Haq, I. U. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.08546

Innovative deep learning architectures for medical image diagnosis: a comprehensive review of convolutional, recurrent, and transformer models

Ghribi, F., & Hamdaoui, F. (2025) · The Visual Computer · doi:10.1007/s00371-025-04122-1 · Cited by 21 · see citing works

A Comprehensive Review of Deep Learning in Medical Image Analysis (Preprint)Preprint

Gajanayake, I. (2025) · doi:10.2196/preprints.88071

Review on Deep Learning based Medical Image Processing

Agarwal, A., Kumar, R., & Gupta, M. (2022) · IEEE International Conference on Current Development in Engineering and Technology (CCET) · doi:10.1109/ccet56606.2022.10080703 · Cited by 30 · see citing works

Deep Learning for Disease Detection in Medical Imaging: From Classification to Segmentation and Localization

Shrivastava, A., Al‐Jawahry, H. M., Lakhanpal, S., Varshney, N., Mathur, N., & Parmar, A. (2025) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi67124.2025.11403227