Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Statistical Comparisons of Classifiers over Multiple Data Sets

Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 11,966 · see citing works

Dataset of breast ultrasound images

Al-Dhabyani, W., Gomaa, M., Khaled, H., & Fahmy, A. (2020) · Data in Brief · doi:10.1016/j.dib.2019.104863 · Cited by 2,405 · see citing works

Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis

Feng, Y., Spezia, M., Huang, S., Yuan, C., Zeng, Z., Zhang, L., Ji, X., Liu, W., Huang, B., Luo, W., et al. (2018) · Genes & Diseases · doi:10.1016/j.gendis.2018.05.001 · Cited by 1,538 · see citing works

Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network

Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328 · see citing works

Computer‐aided diagnosis of breast ultrasound images using ensemble learning from convolutional neural networks

Moon, W. K., Lee, Y., Ke, H.-H., Lee, S. H., Huang, C., & Chang, R. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105361 · Cited by 322 · see citing works

A decision support system for multimodal brain tumor classification using deep learning

Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00321-0 · Cited by 305 · see citing works

Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives

Geras, K. J., Mann, R. M., & Moy, L. P. (2019) · Radiology · doi:10.1148/radiol.2019182627 · Cited by 298 · see citing works

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Sun, Q., Lin, X., Zhao, Y., Li, L., Yan, K., Liang, D., Sun, D., & Li, Z. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00053 · Cited by 264 · see citing works

Ultrasound for Breast Cancer Detection Globally: A Systematic Review and Meta-Analysis

Sood, R., Rositch, A. F., Shakoor, D., Ambinder, E. B., Pool, K.-L., Pollack, E. B., Mollura, D. J., Mullen, L. A., & Harvey, S. C. (2019) · Journal of Global Oncology · doi:10.1200/jgo.19.00127 · Cited by 261 · see citing works

Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network

Odusami, M., Maskeliūnas, R., Damaševičius, R., & Krilavičius, T. (2021) · Diagnostics · doi:10.3390/diagnostics11061071 · Cited by 245 · see citing works

Convolutional neural network-based models for diagnosis of breast cancer

Masud, M., Rashed, A. E. E., & Hossain, M. S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05394-5 · Cited by 185 · see citing works

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Lahoura, V., Singh, H., Aggarwal, A., Sharma, B., Mohammed, M. A., Damaševičius, R., Kadry, S., & Cengiz, K. (2021) · Diagnostics · doi:10.3390/diagnostics11020241 · Cited by 185 · see citing works

Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection

Khan, M. A., Sharif, M. I., Raza, M., Anjum, A., Saba, T., & Shad, S. A. (2019) · Expert Systems · doi:10.1111/exsy.12497 · Cited by 171 · see citing works

Multi-Class Skin Lesion Detection and Classification via Teledermatology

Khan, M. A., Muhammad, K., Sharif, M. I., Akram, T., & de Albuquerque, V. H. C. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3067789 · Cited by 169 · see citing works

Development of a Deep-Learning-Based Method for Breast Ultrasound Image Segmentation

Almajalid, R., Shan, J., Du, Y., & Zhang, M. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00179 · Cited by 157 · see citing works

Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification

Pang, T., Wong, J. H. D., Ng, W. L., & Chan, C. S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106018 · Cited by 147 · see citing works

The Automated Learning of Deep Features for Breast Mass Classification from Mammograms

Dhungel, N., Carneiro, G., & Bradley, A. P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46723-8_13 · Cited by 141 · see citing works

Clinical Implications of Tumor-Infiltrating Immune Cells in Breast Cancer

Zhang, S., Hu, Z., Long, J., Zhu, G., Wang, Y., Jia, Y., Zhou, J., Ouyang, Y., & Zeng, Z. (2019) · Journal of Cancer · doi:10.7150/jca.35901 · Cited by 141 · see citing works

Prediction of COVID-19 - Pneumonia based on Selected Deep Features and One Class Kernel Extreme Learning Machine

Khan, M. A., Kadry, S., Zhang, Y., Akram, T., Sharif, M., Rehman, A. U., & Saba, T. (2020) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106960 · Cited by 130 · see citing works

New Frontiers: An Update on Computer-Aided Diagnosis for Breast Imaging in the Age of Artificial Intelligence

Gao, Y., Geras, K. J., Lewin, A. A., & Moy, L. P. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20392 · Cited by 125 · see citing works

Breast Ultrasound Computer-Aided Diagnosis Using BI-RADS Features

Shen, W.-C., Chang, R., Moon, W. K., Chou, Y., & Huang, C. (2007) · Academic Radiology · doi:10.1016/j.acra.2007.04.016 · Cited by 123 · see citing works

Breast ultrasound tumour classification: A Machine Learning—Radiomics based approach

Mishra, A. K., Roy, P., Bandyopadhyay, S., & Das, S. K. (2021) · Expert Systems · doi:10.1111/exsy.12713 · Cited by 109 · see citing works

Breast ultrasound tumor image classification using image decomposition and fusion based on adaptive multi-model spatial feature fusion

Zhuang, Z., Yang, Z., Raj, A. N. J., Wei, C., Jin, P., & Zhuang, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106221 · Cited by 106 · see citing works

Dilated Semantic Segmentation for Breast Ultrasonic Lesion Detection Using Parallel Feature Fusion

Irfan, R., Almazroi, A. A., Rauf, H. T., Damaševičius, R., Nasr, E. A., & Abdelgawad, A. (2021) · Diagnostics · doi:10.3390/diagnostics11071212 · Cited by 104 · see citing works

Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold

Abayomi‐Alli, O., Damaševičius, R., Misra, S., Maskeliūnas, R., & Abayomi-Alli, A. A. (2021) · TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES · doi:10.3906/elk-2101-133 · Cited by 100 · see citing works

Signal and Image Multiresolution Analysis

(2012) · Wiley eBooks · doi:10.1002/9781118568767 · Cited by 93 · see citing works

Breast mass classification with transfer learning based on scaling of deep representations

Byra, M. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102828 · Cited by 91 · see citing works

The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review

Fujioka, T., Mori, M., Kubota, K., Oyama, J., Yamaga, E., Yashima, Y., Katsuta, L., Nomura, K., Nara, M., Oda, G., et al. (2020) · Diagnostics · doi:10.3390/diagnostics10121055 · Cited by 90 · see citing works

ABC of breast diseases: Breast cancer

Sainsbury, J. R. C. (2000) · BMJ · doi:10.1136/bmj.321.7263.745 · Cited by 89 · see citing works

An Efficient Approach for the Detection of Brain Tumor Using Fuzzy Logic and U-NET CNN Classification

Maqsood, S., Damaševičius, R., & Shah, F. M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86976-2_8 · Cited by 89 · see citing works

COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion

Khan, M. A., Alhaisoni, M. M., Tariq, U., Hussain, N., Majid, A., Damaševičius, R., & Maskeliūnas, R. (2021) · Sensors · doi:10.3390/s21217286 · Cited by 85 · see citing works

Ear Recognition Based on Deep Unsupervised Active Learning

Khaldi, Y., Benzaoui, A., Ouahabi, A., Jacques, S., & Ahmed, A. T. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3100151 · Cited by 83 · see citing works

Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study

Badawy, S. M., Mohamed, A. E. A., Hefnawy, A. A., Zidan, H. E., GadAllah, M. T., & El‐Banby, G. M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251899 · Cited by 79 · see citing works

Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients

Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2021) · Sensors · doi:10.3390/s21113865 · Cited by 75 · see citing works

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

Zahoor, S., Lali, I. U., Khan, M. A., Javed, K., & Mehmood, W. (2020) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405616666200406110547 · Cited by 71 · see citing works

Human gait analysis for osteoarthritis prediction: a framework of deep learning and kernel extreme learning machine

Khan, M. A., Kadry, S., Parwekar, P., Damaševičius, R., Mehmood, A., Khan, J. A., & Naqvi, S. R. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00244-2 · Cited by 71 · see citing works

Breast-Cancer Detection using Thermal Images with Marine-Predators-Algorithm Selected Features

Rajinikanth, V., Kadry, S., Taniar, D., Damaševičius, R., & Rauf, H. T. (2021) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii51839.2021.9445166 · Cited by 68 · see citing works

A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning

Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.05.007 · Cited by 66 · see citing works

3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data

Alinsaif, S., & Lang, J. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104879 · Cited by 66 · see citing works

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry, S., Rajinikanth, V., Taniar, D., Damaševičius, R., & Valencia, X. P. B. (2021) · The Journal of Supercomputing · doi:10.1007/s11227-021-04125-4 · Cited by 65 · see citing works

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

Sadad, T., Hussain, A., Munir, A., Habib, M., Khan, S. A., Hussain, S., Yang, S., & Alawairdhi, M. (2020) · Applied Sciences · doi:10.3390/app10061900 · Cited by 57 · see citing works

Nonparametric Denoising Methods Based on Contourlet Transform with Sharp Frequency Localization: Application to Low Exposure Time Electron Microscopy Images

Ahmed, S. S., Messali, Z., Ouahabi, A., Trépout, S., Messaoudi, C., & Marco, S. (2015) · Entropy · doi:10.3390/e17053461 · Cited by 56 · see citing works

Breast Ultrasound Image Classification Based on Multiple-Instance Learning

Ding, J., Cheng, H. D., Huang, J., Liu, J., & Zhang, Y. (2012) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-012-9499-x · Cited by 55 · see citing works

Comparative assessment of texture features for the identification of cancer in ultrasound images: a review

Faust, O., Acharya, U. R., Meiburger, K. M., Molinari, F., Koh, J. E. W., Yeong, C. H., Kongmebhol, P., & Ng, K. H. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.01.001 · Cited by 53 · see citing works

Analysis of Brain MRI Images Using Improved CornerNet Approach

Nawaz, M., Nazir, T., Masood, M., Mehmood, A., Mahum, R., Khan, M. A., Kadry, S., & Thinnukool, O. (2021) · Diagnostics · doi:10.3390/diagnostics11101856 · Cited by 51 · see citing works

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Moustafa, A. F., Cary, T. W., Sultan, L. R., Schultz, S. M., Conant, E. F., Venkatesh, S. S., & Sehgal, C. M. (2020) · Diagnostics · doi:10.3390/diagnostics10090631 · Cited by 50 · see citing works

Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network

Meng, L., Zhang, Q., & Bu, S. (2021) · Diagnostics · doi:10.3390/diagnostics11101806 · Cited by 40 · see citing works

Dual-modal computer-assisted evaluation of axillary lymph node metastasis in breast cancer patients on both real-time elastography and B-mode ultrasound

Zhang, Q., Suo, J., Chang, W., Shi, J., & Chen, M. (2017) · European Journal of Radiology · doi:10.1016/j.ejrad.2017.07.027 · Cited by 39 · see citing works

Automated Segmentation and Severity Analysis of Subdural Hematoma for Patients with Traumatic Brain Injuries

Farzaneh, N., Williamson, C. A., Jiang, C., Srinivasan, A., Bapuraj, J. R., Gryak, J., Najarian, K., & Soroushmehr, S. M. R. (2020) · Diagnostics · doi:10.3390/diagnostics10100773 · Cited by 38 · see citing works

Presentation of Novel Architecture for Diagnosis and Identifying Breast Cancer Location Based on Ultrasound Images Using Machine Learning

Pourasad, Y., Zarouri, E., Parizi, M. S., & Mohammed, A. S. (2021) · Diagnostics · doi:10.3390/diagnostics11101870 · Cited by 36 · see citing works

Breast tumor classification through learning from noisy labeled ultrasound images

Cao, Z., Yang, G., Chen, Q., Chen, X., & Lv, F. (2019) · Medical Physics · doi:10.1002/mp.13966 · Cited by 35 · see citing works

Extraction of Tumour in Breast MRI using Joint Thresholding and Segmentation – A Study

Kadry, S., Damaševičius, R., Taniar, D., Rajinikanth, V., & Lawal, I. A. (2021) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii51839.2021.9445152 · Cited by 34 · see citing works

Contextual Level-Set Method for Breast Tumor Segmentation

Hussain, S., Xi, X., Ullah, I., Wu, Y., Ren, C., Lianzheng, Z., Tian, C., & Yin, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3029684 · Cited by 33 · see citing works

Tumor Segmentation in Breast Ultrasound Image by Means of Res Path Combined with Dense Connection Neural Network

Yu, K., Chen, S., & Chen, Y. (2021) · Diagnostics · doi:10.3390/diagnostics11091565 · Cited by 33 · see citing works

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad, M., Zeebaree, D. Q., Brifcani, A. M. A., Saeed, J. N., & Zebari, D. A. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/2020jastt1328 · Cited by 30 · see citing works

Computer-aided Diagnosis in Breast Ultrasound

Chen, D., & Hsiao, Y.-H. (2008) · Journal of Medical Ultrasound · doi:10.1016/s0929-6441(08)60005-3 · Cited by 29 · see citing works

COVID19 Classification Using CT Images via Ensembles of Deep Learning Models

Majid, A., Khan, M. A., Nam, Y., Tariq, U., Roy, S. S., Mostafa, R. R., & Sakr, R. H. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.016816 · Cited by 27 · see citing works

Automatic detection and classification of benign and malignant lesions in breast ultrasound images using texture morphological and fractal features

Prabhakar, T., & Poonguzhali, S. (2017) · Biomedical Engineering International Conference · doi:10.1109/bmeicon.2017.8229114 · Cited by 23 · see citing works

Fusion of Handcrafted and Deep Features for Medical Image Classification

Liu, D., Liu, Y., Li, S., Li, W., & Wang, L. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1345/2/022052 · Cited by 21 · see citing works

Fourier-based shape feature extraction technique for computer-aided B-Mode ultrasound diagnosis of breast tumor

Lee, J., Seong, Y. K., Chang, C.-H., Park, J., Park, M., Woo, K.-G., & Ko, E. Y. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6347495 · Cited by 19 · see citing works

Shape-Adaptive Convolutional Operator for Breast Ultrasound Image Segmentation

Huang, K., Zhang, Y., Cheng, H.-D., & Xing, P. (2021) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme51207.2021.9428287 · Cited by 19 · see citing works

Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review

Jiménez-Gaona, Y., Rodríguez-Álvarez, M. J., & Lakshminarayanan, V. (2020) · Applied Sciences · doi:10.3390/app10228298 · Cited by 19 · see citing works

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification

Lu, B., & Wang, W. (2017) · Computational and Mathematical Methods in Medicine · doi:10.1155/2017/7894705 · Cited by 17 · see citing works

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang, X., Hu, Z., Wang‎, S., & Zhang, Y. (2023) · Cancers · doi:10.3390/cancers15143608 · Cited by 390 · see citing works

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Xu, X., Li, J., Zhu, Z., Zhao, L., Wang, H., Song, C., Chen, Y., Zhao, Q., Yang, J., & Pei, Y. (2024) · Bioengineering · doi:10.3390/bioengineering11030219 · Cited by 242 · see citing works

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Iqbal, S., Qureshi, A. N., Li, J., & Mahmood, T. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09899-9 · Cited by 233 · see citing works

A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning

Abhisheka, B., Biswas, S. K., & Purkayastha, B. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09968-z · Cited by 175 · see citing works

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022) · Applied Sciences · doi:10.3390/app12073273 · Cited by 165 · see citing works

Intelligent Hybrid Deep Learning Model for Breast Cancer Detection

Wang, X., Ahmad, I., Javeed, D., Zaidi, S. F. B., Alotaibi, F. M., Ghoneim, M. E., Daradkeh, Y. I., Asghar, J., & Eldin, S. M. (2022) · Electronics · doi:10.3390/electronics11172767 · Cited by 147 · see citing works

Overview of Artificial Intelligence in Breast Cancer Medical Imaging

Zheng, D., He, X., & Jing, J. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020419 · Cited by 113 · see citing works

The Role of Deep Learning in Advancing Breast Cancer Detection Using Different Imaging Modalities: A Systematic Review

Madani, M., Behzadi, M. M., & Nabavi, S. (2022) · Cancers · doi:10.3390/cancers14215334 · Cited by 122 · see citing works

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

Jabeen, K., Khan, M. A., Balili, J., Alhaisoni, M. M., Almujally, N. A., Alrashidi, H., Tariq, U., & Cha, J. (2023) · Diagnostics · doi:10.3390/diagnostics13071238 · Cited by 110 · see citing works

Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks

Ali, M. D., Saleem, A., Elahi, H., Khan, M. A., Khan, M. I., Yaqoob, M. M., Khattak, U. F., & Al‐Rasheed, A. (2023) · Diagnostics · doi:10.3390/diagnostics13132242 · Cited by 104 · see citing works

BreaST-Net: Multi-Class Classification of Breast Cancer from Histopathological Images Using Ensemble of Swin Transformers

Tummala, S., Kim, J., & Kadry, S. (2022) · Mathematics · doi:10.3390/math10214109 · Cited by 99 · see citing works

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Rahman, H., Bukht, T. F. N., Ahmad, R. B., Almadhor, A. S., & Javed, A. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/7717712 · Cited by 95 · see citing works

A Multi-Agent Deep Reinforcement Learning Approach for Enhancement of COVID-19 CT Image Segmentation

Allioui, H., Mohammed, M. A., Benameur, N., Al‐Khateeb, B., Abdulkareem, K. H., García-Zapirain, B., Damaševičius, R., & Maskeliūnas, R. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020309 · Cited by 92 · see citing works

PoxNet22: A Fine-Tuned Model for the Classification of Monkeypox Disease Using Transfer Learning

Yasmin, F., Hassan, M. M., Hasan, M., Zaman, S., Kaushal, C., El‐Shafai, W., & Soliman, N. F. (2023) · IEEE Access · doi:10.1109/access.2023.3253868 · Cited by 87 · see citing works

Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI

Latha, M. S., Kumar, P. S., Chandrika, R. R., Mahesh, T. R., Kumar, V., & Guluwadi, S. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01404-3 · Cited by 86 · see citing works

Mammography and ultrasound based dual modality classification of breast cancer using a hybrid deep learning approach

Atrey, K., Singh, B. K., Bodhey, N. K., & Pachori, R. B. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104919 · Cited by 85 · see citing works

Denoising and segmentation in medical image analysis: A comprehensive review on machine learning and deep learning approaches

Kumar, R. R., & Priyadarshi, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19313-6 · Cited by 82 · see citing works

Applying Deep Learning for Breast Cancer Detection in Radiology

Mahoro, E., & Akhloufi, M. A. (2022) · Current Oncology · doi:10.3390/curroncol29110690 · Cited by 69 · see citing works

A Comprehensive Review on Smart Health Care: Applications, Paradigms, and Challenges with Case Studies

Raoof, S. S., & Durai, M. A. S. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/4822235 · Cited by 64 · see citing works

Breast cancer diagnosis: A systematic review

Wen, X., Guo, X., Wang‎, S., Lu, Z., & Zhang, Y. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.01.002 · Cited by 64 · see citing works

Predicting Breast Tumor Malignancy Using Deep ConvNeXt Radiomics and Quality-Based Score Pooling in Ultrasound Sequences

Hassanien, M. A. M., Singh, V. K., Puig, D., & Abdel‐Nasser, M. (2022) · Diagnostics · doi:10.3390/diagnostics12051053 · Cited by 61 · see citing works

Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm

Atrey, K., Singh, B. K., & Bodhey, N. K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16414-6 · Cited by 61 · see citing works

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset

Murty, P. S. R. C., Anuradha, C., Naidu, P. A., Mandru, D., Ashok, M., Atheeswaran, A., Rajeswaran, N., & Saravanan, V. (2024) · Scientific Reports · doi:10.1038/s41598-024-74305-8 · Cited by 59 · see citing works

InceptionNeXt-Transformer: A novel multi-scale deep feature learning architecture for multimodal breast cancer diagnosis

Paçal, İ., & Attallah, O. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108116 · Cited by 58 · see citing works

A survey on deep learning in medical ultrasound imaging

Song, K., Feng, J., & Chen, D. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1398393 · Cited by 59 · see citing works

Benign and Malignant Breast Tumor Classification in Ultrasound and Mammography Images via Fusion of Deep Learning and Handcraft Features

Cruz-Ramos, C., García-Avila, O., Almaraz-Damian, J. A., Ponomaryov, V. I., Reyes-Reyes, R., & Sadovnychiy, S. (2023) · Entropy · doi:10.3390/e25070991 · Cited by 55 · see citing works

Deep Learning Approaches for Classification of Breast Cancer in Ultrasound (US) Images

Paçal, İ. (2022) · Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi · doi:10.21597/jist.1183679 · Cited by 52 · see citing works

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Aamir, S., Rahim, A., Aamir, Z., Abbasi, S. F., Khan, M. S., Alhaisoni, M. M., Khan, M. A., Khan, K., & Ahmad, J. E. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/5869529 · Cited by 50 · see citing works

Enhancing Breast Cancer Classification in Histopathological Images through Federated Learning Framework

Peta, J., & Koppu, S. (2023) · IEEE Access · doi:10.1109/access.2023.3283930 · Cited by 50 · see citing works

Multimodal breast cancer hybrid explainable computer-aided diagnosis using medical mammograms and ultrasound Images

Al-Tam, R. M., Al-Hejri, A. M., Alshamrani, S. S., Al–antari, M. A., & Narangale, S. M. (2024) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2024.08.007 · Cited by 48 · see citing works

Breast UltraSound Image classification using fuzzy-rank-based ensemble network

Deb, S. D., & Jha, R. K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104871 · Cited by 49 · see citing works

A Review of Artificial Intelligence in Breast Imaging

Al-Karawi, D., Al-Zaidi, S., Helael, K. A., Obeidat, N., Mouhsen, A. M., Ajam, T., Alshalabi, B. A., Salman, M., & Ahmed, M. H. (2024) · Tomography · doi:10.3390/tomography10050055 · Cited by 49 · see citing works

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Afrin, H., Larson, N. B., Fatemi, M., & Alizad, A. (2023) · Cancers · doi:10.3390/cancers15123139 · Cited by 46 · see citing works

LMHistNet: Levenberg–Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images

Koshy, S. S., & Anbarasi, L. J. (2024) · IEEE Access · doi:10.1109/access.2024.3385011 · Cited by 45 · see citing works

Artificial intelligence for breast cancer: Implications for diagnosis and management

AlSamhori, J. F., AlSamhori, A. R. F., Duncan, L. A., Qalajo, A., Alshahwan, H. F., Al-abbadi, M., Soudi, M. A., Zakraoui, R., AlSamhori, A. F., AlRyalat, S. A., et al. (2024) · Journal of Medicine Surgery and Public Health · doi:10.1016/j.glmedi.2024.100120 · Cited by 44 · see citing works

Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset

Adedigba, A. P., Adeshina, S. A., & Aibinu, A. M. (2022) · Bioengineering · doi:10.3390/bioengineering9040161 · Cited by 44 · see citing works

An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection

Muduli, D., Kumar, R. R., Pradhan, J., & Kumar, A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08992-1 · Cited by 43 · see citing works

Deep learning radiomics based on multimodal imaging for distinguishing benign and malignant breast tumours

Lu, G., Tian, R., Yang, W. P., Liu, R., Liu, D., Xiang, Z., & Zhang, G. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1402967 · Cited by 46 · see citing works

The role of explainable AI in enhancing breast cancer diagnosis using machine learning and deep learning models

Ansari, Z. A., Tripathi, M. M., & Ahmed, R. (2025) · Discover Artificial Intelligence · doi:10.1007/s44163-025-00307-8 · Cited by 50 · see citing works

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Pati, A., Parhi, M., Pattanayak, B. K., Singh, D., Singh, V. P., Kadry, S., Nam, Y., & Kang, B.-G. (2023) · Diagnostics · doi:10.3390/diagnostics13132191 · Cited by 42 · see citing works

Automated breast tumor ultrasound image segmentation with hybrid UNet and classification using fine-tuned CNN model

Hossain, S., Azam, S., Montaha, S., Karim, A., Chowa, S. S., Mondol, C., Hasan, M. Z., & Jonkman, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e21369 · Cited by 42 · see citing works

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Baghdadi, N. A., Malki, A., Balaha, H. M., AbdulAzeem, Y., Badawy, M., & Elhosseini, M. A. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1054 · Cited by 41 · see citing works

Breast cancer detection using deep learning techniques: challenges and future directions

Shahid, M. S., & Imran, A. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20606-7 · Cited by 43 · see citing works

Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms

Basurto-Hurtado, J. A., Cruz-Albarrán, I. A., Toledano‐Ayala, M., Ibarra-Manzano, M.-A., Morales-Hernandez, L. A., & Perez-Ramirez, C. A. (2022) · Cancers · doi:10.3390/cancers14143442 · Cited by 46 · see citing works

B 2 C 3 NetF 2 : Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Fatima, M., Khan, M. A., Shaheen, S., Almujally, N. A., & Wang‎, S. (2023) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12219 · Cited by 40 · see citing works

Intelligent Ultrasound Imaging for Enhanced Breast Cancer Diagnosis: Ensemble Transfer Learning Strategies

Rao, K. S., Terlapu, P. V., Jayaram, D. K., Raju, K. K., Kumar, G., Pemula, R., Gopalachari, M. V., & Rakesh, S. (2024) · IEEE Access · doi:10.1109/access.2024.3358448 · Cited by 40 · see citing works

A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms

Mohamed, E., Gaber, T., Karam, O. H., & Rashed, E. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0276523 · Cited by 39 · see citing works

Comparative evaluation of automated machine learning techniques for breast cancer diagnosis

Rashed, A. E. E., Elmorsy, A. M., & Atwa, A. E. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105016 · Cited by 34 · see citing works

Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images

Cho, Y., Misra, S., Managuli, R., Barr, R. G., Lee, J., & Kim, C. (2024) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2024.11.020 · Cited by 36 · see citing works

Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted

Al-Jabbar, M., Alshahrani, M., Senan, E. M., & Ahmed, I. A. (2023) · Diagnostics · doi:10.3390/diagnostics13101753 · Cited by 32 · see citing works

Advanced Hybridization and Optimization of DNNs for Medical Imaging: A Survey on Disease Detection Techniques

Bohmrah, M. K., & Kaur, H. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11049-x · Cited by 30 · see citing works

Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling

Pathan, R. K., Alam, F. I., Yasmin, S., Hamd, Z. Y., Aljuaid, H., Khandaker, M. U., & Lau, S. L. (2022) · Healthcare · doi:10.3390/healthcare10122367 · Cited by 30 · see citing works

Classification of Breast Cancer Ultrasound Images with Deep Learning-Based Models

Uysal, F., & Köse, M. M. (2022) · International Electronic Conference on Applied Sciences (ASEC) · doi:10.3390/asec2022-13791 · Cited by 30 · see citing works

Breast cancer detection and classification using metaheuristic optimized ensemble extreme learning machine

Pattanaik, R. K., Siddique, M., Mishra, S., Gelmecha, D. J., Singh, R. S., & Satapathy, S. (2023) · International Journal of Information Technology · doi:10.1007/s41870-023-01533-y · Cited by 29 · see citing works

Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

Ali, M. J., Shah, J. H., Khan, M. A., Alhaisoni, M. M., Tariq, U., Akram, T., Kim, Y. J., & Chang, B. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.030392 · Cited by 29 · see citing works

DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image

Zhu, C., Chai, X., Wang, Z., Xiao, Y., Zhang, R., Yang, Z., & Feng, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106221 · Cited by 29 · see citing works

Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features

Chowa, S. S., Azam, S., Montaha, S., Payel, I. J., Bhuiyan, M. R. I., Hasan, M. Z., & Jonkman, M. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-05464-w · Cited by 26 · see citing works

Enhancing Breast Cancer Detection in Ultrasound Images: An Innovative Approach Using Progressive Fine‐Tuning of Vision Transformer Models

Alruily, M., Mahmoud, A. A., Allahem, H., Mostafa, A. M., Shabana, H., & Ezz, M. M. (2024) · International Journal of Intelligent Systems · doi:10.1155/int/6528752 · Cited by 26 · see citing works

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Masood, H., Zafar, A., Ali, M. U., Hussain, T., Khan, M. A., Tariq, U., & Damaševičius, R. (2022) · Sensors · doi:10.3390/s22031098 · Cited by 26 · see citing works

An updated overview of radiomics-based artificial intelligence (AI) methods in breast cancer screening and diagnosis

Elahi, R., & Nazari, M. (2024) · Radiological Physics and Technology · doi:10.1007/s12194-024-00842-6 · Cited by 27 · see citing works

BRMI-Net: Deep Learning Features and Flower Pollination-Controlled Regula Falsi-Based Feature Selection Framework for Breast Cancer Recognition in Mammography Images

Rehman, S. U., Khan, M. A., Masood, A., Almujally, N. A., Baili, J., Alhaisoni, M. M., Tariq, U., & Zhang, Y. (2023) · Diagnostics · doi:10.3390/diagnostics13091618 · Cited by 25 · see citing works

Breast cancer diagnosis based on hybrid SqueezeNet and improved chef-based optimizer

Huang, Q., Ding, H. W., & Effatparvar, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121470 · Cited by 25 · see citing works

A Method for Ocular Disease Diagnosis through Visual Prediction Explainability

Santone, A., Cesarelli, M., Colasuonno, E., Bevilacqua, V., & Mercaldo, F. (2024) · Electronics · doi:10.3390/electronics13142706 · Cited by 25 · see citing works

Deep Learning-assisted Diagnosis of Breast Lesions on US Images: A Multivendor, Multicenter Study

Xiang, H., Wang, X., Xu, M., Zhang, Y., Zeng, S., Li, C., Liu, L., Deng, T., Tang, G., Yan, C., et al. (2023) · Radiology Artificial Intelligence · doi:10.1148/ryai.220185 · Cited by 25 · see citing works

Diagnosis of Cubital Tunnel Syndrome Using Deep Learning on Ultrasonographic Images

Shinohara, I., Inui, A., Mifune, Y., Nishimoto, H., Yamaura, K., Mukohara, S., Yoshikawa, T., Kato, T., Furukawa, T., Hoshino, Y. Y., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030632 · Cited by 24 · see citing works

State-of-the-Art of Breast Cancer Diagnosis in Medical Images via Convolutional Neural Networks (CNNs)

Harrison, P., Hasan, R., & Park, K. (2023) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-023-00144-3 · Cited by 26 · see citing works

Improved early detection accuracy for breast cancer using a deep learning framework in medical imaging

RICHA, R., & Patro, B. D. K. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109751 · Cited by 22 · see citing works

UCapsNet: A Two-Stage Deep Learning Model Using U-Net and Capsule Network for Breast Cancer Segmentation and Classification in Ultrasound Imaging

Madhu, G., Bonasi, A. M., Kautish, S., Almazyad, A. S., Mohamed, A. W., Werner, F. M., Hosseinzadeh, M., & Shokouhifar, M. (2024) · Cancers · doi:10.3390/cancers16223777 · Cited by 21 · see citing works

Artificial intelligence integrates multi-omics data for precision stratification and drug resistance prediction in breast cancer

Ran, D., Li, J., Zhao, M., Du, L., Zhang, Y., & Zhu, J. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1612474 · Cited by 23 · see citing works

Using Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames

Stephen, O., & Sain, M. (2023) · Journal of Healthcare Engineering · doi:10.1155/2023/4597445 · Cited by 21 · see citing works

Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Zhenfei, W., Ali, M. M., Sahibzada, K. I., Maqsood, F., Rehman, N. U., Aftab, M., Zia, Q., Hou, W., & Wei, D.-Q. (2024) · Current Bioinformatics · doi:10.2174/0115748936333380240816053223 · Cited by 21 · see citing works

Artificial life for segmentation of fusion ultrasound images of breast abnormalities

Karunanayake, N., Lohitvisate, W., & Makhanov, S. S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108838 · Cited by 19 · see citing works

Degree of Accuracy With Which Deep Learning for Ultrasound Images Identifies Osteochondritis Dissecans of the Humeral Capitellum

Shinohara, I., Yoshikawa, T., Inui, A., Mifune, Y., Nishimoto, H., Mukohara, S., Kato, T., Furukawa, T., Tanaka, S., Kusunose, M., et al. (2023) · The American Journal of Sports Medicine · doi:10.1177/03635465221142280 · Cited by 19 · see citing works

Convolutional Neural Network Deep Learning Model for Improved Ultrasound Breast Tumor Classification

Alrubaie, H. D., Aljobouri, H. K., Al-Jobawi, Z. J., & Çankaya, İ. (2023) · Al-Nahrain Journal for Engineering Sciences · doi:10.29194/njes.26020057 · Cited by 19 · see citing works

CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound

Wang, Y., Wang, C.-Y., Liu, K., Huang, Y.-H., Chen, T., Chiu, K.-N., Liang, C., & Lu, N. (2024) · Tomography · doi:10.3390/tomography10120145 · Cited by 18 · see citing works

Genetic hyperparameter optimization with Modified Scalable-Neighbourhood Component Analysis for breast cancer prognostication

Alsubai, S., Alqahtani, A., & Sha, M. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.02.035 · Cited by 17 · see citing works

Defect Detection Method Based on Knowledge Distillation

Zhou, Q., Wang, H., Tang, Y., & Wang, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3252910 · Cited by 17 · see citing works

FAMF-Net: Feature Alignment Mutual Attention Fusion With Region Awareness for Breast Cancer Diagnosis via Imbalanced Data

Liu, Y., Li, J., Zhao, C., Zhang, Y., Chen, Q., Qin, J., Dong, L., Wang, T., Jiang, W., & Lei, B. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3485612 · Cited by 16 · see citing works

Future of Breast Cancer Diagnosis: A Review of DL and ML Applications and Emerging Trends for Multimodal Data

Abdikenov, B., Zhaksylyk, T., Shortanbaiuly, O., Orazayev, Y., Makhanov, N., Karibekov, T., Suvorov, V., Imasheva, A., Zhumagozhayev, K., & Seitova, A. (2025) · IEEE Access · doi:10.1109/access.2025.3585377 · Cited by 16 · see citing works

Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning

Yıldırım, T. T., Yaman, O., Kılıç, İ., Taşar, B., Timurkaan, E. S., & Aydogdu, N. (2025) · Diagnostics · doi:10.3390/diagnostics15172235 · Cited by 16 · see citing works

Automatic Breast Volume Scanner and B-Ultrasound-Based Radiomics Nomogram for Clinician Management of BI-RADS 4A Lesions

Ma, Q., Wang, J., Xu, D., Zhu, C., Qin, J., Wu, Y., Gao, Y., & Zhang, C. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.11.002 · Cited by 16 · see citing works

Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

Kumar, S. J. K. J., Parthasarathi, P., Hogo, M. A., Masud, M., Al‐Amri, J. F., & Abouhawwash, M. (2023) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2023.033800 · Cited by 16 · see citing works

MV-STCNet: Breast cancer diagnosis using spatial and temporal dual-attention guided classification network based on multi-view ultrasound videos

Kong, D., Hu, S., & Zhao, G. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105541 · Cited by 17 · see citing works

Enhanced Computer-Aided Diagnosis Model on Ultrasound Images through Transfer Learning and Data Augmentation Techniques for an Accurate Breast Tumors Classification

Ahmed, I. B., Ouarda, W., & Amar, C. B. (2023) · Procedia Computer Science · doi:10.1016/j.procs.2023.10.389 · Cited by 16 · see citing works

Breast Cancer Segmentation From Ultrasound Images Using Multiscale Cascaded Convolution With Residual Attention-Based Double Decoder Network

Umer, M. J., Sharif, M. I., & Kim, J. (2024) · IEEE Access · doi:10.1109/access.2024.3429386 · Cited by 17 · see citing works

Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

Karthiga, R., Narasimhan, K., Thanikaiselvan, V., Hemalatha, M., & Amirtharajan, R. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20271-2 · Cited by 19 · see citing works

A Novel Artificial Intelligence Techniques for Women Breast Cancer Classification Using Ultrasound Images

Afrifa, S., Vijayakumar, V., Appiahene, P., & Zhang, T. (2023) · Clinical and Experimental Obstetrics & Gynecology · doi:10.31083/j.ceog5012271 · Cited by 15 · see citing works

Role of inter- and extra-lesion tissue, transfer learning, and fine-tuning in the robust classification of breast lesions

Nastase, I.-N. A., Moldovanu, S., Biswas, K. C., & Moraru, L. (2024) · Scientific Reports · doi:10.1038/s41598-024-74316-5 · Cited by 15 · see citing works

IDC-Net: Breast cancer classification network based on BI-RADS 4

Yi, S., Chen, Z., She, F., Wang, T., Yang, X.-L., Chen, D., & Luo, X. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110323 · Cited by 14 · see citing works

An Efficient USE‐Net Deep Learning Model for Cancer Detection

Almutairi, S. M., Manimurugan, S., Aborokbah, M. M., Narmatha, C., Ganesan, S., & Karthikeyan, P. (2023) · International Journal of Intelligent Systems · doi:10.1155/2023/8509433 · Cited by 14 · see citing works

An Improved Fusion Model from GoogLeNet and AlexNet to Predict Breast Cancer using Deep Learning

Sathishkumar, R., Nirmalraj, M., Vinothini, B., Rajasri, N., & Sivasakthi, E. (2023) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan58655.2023.10395210 · Cited by 13 · see citing works

Artificial rabbits optimization with transfer learning based deepfake detection model for biometric applications

Alazwari, S., Alsamri, M. O. J., Alamgeer, M., Alabdan, R., Alzahrani, I. R., Rizwanullah, M., & Osman, A. E. (2024) · Ain Shams Engineering Journal · doi:10.1016/j.asej.2024.103057 · Cited by 13 · see citing works

Breast cancer classification and segmentation framework using multiscale CNN and U‐shaped dual decoded attention network

Umer, M. J., Sharif, M., & Wang‎, S. (2022) · Expert Systems · doi:10.1111/exsy.13192 · Cited by 15 · see citing works

Review on enhancing clinical decision support system using machine learning

Masood, A., Naseem, U., Rashid, J., Kim, J., & Razzak, I. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12286 · Cited by 14 · see citing works

CNN-based Deep Transfer Learning Approach for Detecting Breast Cancer in Mammogram Images

Zebari, D. A., Haron, H., Sulaiman, D. M., Yusoff, Y. B., & Othman, M. N. M. (2022) · IEEE Conference on Systems, Process and Control · doi:10.1109/icspc55597.2022.10001781 · Cited by 12 · see citing works

Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention

Lu, Y., Sun, F., Wang, J., & Yu, K. M. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1567577 · Cited by 13 · see citing works

Recent Trends in Modalities and Deep Learning Methods for Breast Cancer Detection

Nissar, I., Alam, S., & Masood, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23092-9_33 · Cited by 11 · see citing works

Improve Breast Cancer Classification Based on Deep Feature Fusion and Hyperparameter Customization Using Transfer Learning

Pham, T.-A., Nguyen, T. A., Tran, Q.-V., & Hoang, V.-D. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia240367 · Cited by 2 · see citing works

A Novel CNN-Based Feature Fusion Framework for Breast Cancer Ultrasound Image Classification

Zourhri, M., Cherradi, B., & Khaïli, M. E. (2025) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2025.0160953 · Cited by 1 · see citing works

An optimal deep learning approach for breast cancer detection and classification with pre-trained CNN-based feature learning mechanism

C, M. L., & Prathap, P. M. J. (2024) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2024.2430454

Improving the classification performance of breast ultrasound image using deep learning and optimization algorithm

Khanna, P., Sahu, M., & Singh, B. K. (2021) · IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) · doi:10.1109/tribes52498.2021.9751677 · Cited by 13 · see citing works

Advancing ultrasound breast cancer classification via a dual-backbone fusion model integrating multi-scale attention mechanisms and transformer encoder

Alhaj, Z., & Öztürk, M. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115745