Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning
Abstract
Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.
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The model for brain tumor detection was finalized after analysis of best-performing methods existing in the literature.
“The model was finalized after the analysis of best-performing methods existing in the literature, such as [43,52,53,58,66].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [53]
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
The multimodal brain tumor image segmentation benchmark (BRATS)
Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., et al. (2015) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2014.2377694 · Cited by 6,781 · see citing works
A scaled conjugate gradient algorithm for fast supervised learning
Møller, M. F. (1993) · Neural Networks · doi:10.1016/s0893-6080(05)80056-5 · Cited by 4,094 · see citing works
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 8× · see citing works
“Two pathway CNN model [12] is employed for the prediction of brain tissues [42].”
MethodInfluentialBrain tumor segmentation with Deep Neural Networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., & Larochelle, H. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.05.004 · Cited by 3,278 · see citing works
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735 · see citing works
Two-Dimensional Signal and Image Processing
Lim, J. S. (1989) · Medical Entomology and Zoology · Cited by 2,569 · see citing works
CA: A Cancer Journal for Clinicians
American Cancer Society (2010) · CA A Cancer Journal for Clinicians · doi:10.3322/(issn)1542-4863 · Cited by 2,538 · see citing works
Exciting New Advances in Neuro-Oncology: The Avenue to a Cure for Malignant Glioma
Van Meir, E. G., Hadjipanayis, C. G., Norden, A. D., Shu, H. K., Wen, P. Y., & Olson, J. J. (2010) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.20069 · Cited by 1,372 · see citing works
Deep learning for brain MRI segmentation: state of the art and future directions
Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D. L., & Erickson, B. J. (2017) · Journal of Digital Imaging · doi:10.1007/s10278-017-9983-4 · Cited by 1,129 · see citing works
A survey of MRI-based medical image analysis for brain tumor studies
Bauer, S., Wiest, R., Nolte, L.-P., & Reyes, M. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/13/r97 · Cited by 1,036 · see citing works
Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings.
Aronen, H. J., Gazit, I. E., Louis, D. N., Buchbinder, B. R., Pardo, F. S., Weisskoff, R. M., Harsh, G. R., Cosgrove, G. R., Halpern, E. F., & Hochberg, F. H. (1994) · Radiology · doi:10.1148/radiology.191.1.8134596 · Cited by 905 · see citing works
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
Dong, H., Yang, G., Liu, F., Mo, Y., & Guo, Y. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_44 · Cited by 868 · see citing works
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
Zhao, X., Wu, Y., Song, G., Li, Z., Zhang, Y., & Fan, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2017.10.002 · Cited by 813 · see citing works
Bahadure, N. B., Ray, A. K., & Thethi, H. P. (2017) · International Journal of Biomedical Imaging · doi:10.1155/2017/9749108 · Cited by 617 · see citing works
Brain Tumor Type Classification via Capsule Networks
Afshar, P., Mohammadi, A., & Plataniotis, K. N. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451379 · Cited by 578 · see citing works
A distinctive approach in brain tumor detection and classification using MRI
Amin, J., Sharif, M. I., Yasmin, M., & Fernandes, S. L. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.10.036 · Cited by 501 · see citing works
Self-citationAutomatic tumor segmentation using knowledge-based techniques
Clark, M. C., Hall, L., Goldgof, D. B., Velthuizen, R. P., Murtagh, F. R., & Silbiger, M. S. (1998) · IEEE Transactions on Medical Imaging · doi:10.1109/42.700731 · Cited by 461 · see citing works
Provenzale, J. M., Mukundan, S., & Barboriak, D. P. (2006) · Radiology · doi:10.1148/radiol.2393042031 · Cited by 399 · see citing works
MR diffusion imaging of human intracranial tumours
Krabbe, K. S., Gideon, P., Wagn, P., Hansen, U., Thomsen, C., & Madsen, F. (1997) · Neuroradiology · doi:10.1007/s002340050450 · Cited by 327 · see citing works
Big data analysis for brain tumor detection: Deep convolutional neural networks
Amin, J., Sharif, M., Yasmin, M., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.04.065 · Cited by 327 · see citing works
Self-citationFully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks
Chen, L., Bentley, P., & Rueckert, D. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.06.016 · Cited by 305 · see citing works
Tustison, N. J., Shrinidhi, K. L., Wintermark, M., Durst, C. R., Kandel, B. M., Gee, J. C., Grossman, M., & Avants, B. (2014) · Neuroinformatics · doi:10.1007/s12021-014-9245-2 · Cited by 277 · see citing works
Rajinikanth, V., Satapathy, S. C., Fernandes, S. L., & Nachiappan, S. (2017) · Pattern Recognition Letters · doi:10.1016/j.patrec.2017.05.028 · Cited by 238 · see citing works
(2014) · Cited by 234 · see citing works
Intraaxial Brain Masses: MR Imaging–based Diagnostic Strategy—Initial Experience
Alokaili, R. N., Krejza, J., Woo, J. H., Wolf, R. L., O’Rourke, D. M., Judy, K. D., Poptani, H., & Melhem, E. R. (2007) · Radiology · doi:10.1148/radiol.2432060493 · Cited by 229 · see citing works
Brain Tumor Segmentation Based on Local Independent Projection-Based Classification
Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., & Feng, Q. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2325410 · Cited by 219 · see citing works
Dual-force convolutional neural networks for accurate brain tumor segmentation
Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195 · see citing works
A framework for offline signature verification system: Best features selection approach
Sharif, M. I., Khan, M. A., Faisal, M. S., Yasmin, M., & Fernandes, S. L. (2018) · Pattern Recognition Letters · doi:10.1016/j.patrec.2018.01.021 · Cited by 191 · see citing works
Self-citation(2014) · Cited by 188 · see citing works
1H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors
Cho, Y.-D., Choi, G.-H., Lee, S.-P., & Kim, J.-K. (2003) · Magnetic Resonance Imaging · doi:10.1016/s0730-725x(03)00097-3 · Cited by 159 · see citing works
Molecular diagnostics of gliomas: the clinical perspective
Tabatabai, G., Stupp, R., van den Bent, M. J., Hegi, M. E., Tonn, J. C., Wick, W., & Weller, M. (2010) · Acta Neuropathologica · doi:10.1007/s00401-010-0750-6 · Cited by 155 · see citing works
Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N. K., & Ahuja, C. K. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2016.05.020 · Cited by 148 · see citing works
Wu, W. Y., Chen, A. Y. C., Zhao, L., & Corso, J. J. (2013) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0922-7 · Cited by 146 · see citing works
Amin, J., Sharif, M., Yasmin, M., Ali, H., & Fernandes, S. L. (2017) · Journal of Computational Science · doi:10.1016/j.jocs.2017.01.002 · Cited by 142 · see citing works
Self-citationFluid Vector Flow and Applications in Brain Tumor Segmentation
Wang, T., Cheng, I., & Basu, A. (2009) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2009.2012423 · Cited by 139 · see citing works
Verma, R., Zacharaki, E. I., Ou, Y., Cai, H., Chawla, S., Lee, S., Melhem, E. R., Wolf, R. L., & Davatzikos, C. A. (2008) · Academic Radiology · doi:10.1016/j.acra.2008.01.029 · Cited by 137 · see citing works
A novel content-based active contour model for brain tumor segmentation
Sachdeva, J., Kumar, V., Gupta, I., Khandelwal, N. K., & Ahuja, C. K. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.01.006 · Cited by 130 · see citing works
Liaqat, A., Khan, M. A., Shah, J. H., SHARIF, M., Yasmin, M., & Fernandes, S. L. (2018) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519418500380 · Cited by 125 · see citing works
Self-citationUnsupervised Detection of Lesions in Brain MRI using Constrained Adversarial Auto-encoders
Chen, X., & Konukoglu, E. (2018) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000321650 · Cited by 125 · see citing works
IX. MR tissue characterization of intracranial tumors by means of texture analysis
Schad, L. R., Blüml, S., & ZUNA, I. (1993) · Magnetic Resonance Imaging · doi:10.1016/0730-725x(93)90206-s · Cited by 123 · see citing works
Dvořák, P., & Menze, B. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-42016-5_6 · Cited by 119 · see citing works
Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encodersPreprint
Chen, X., & Konukoglu, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.04972 · Cited by 118 · see citing works
Fernandes, S. L., Tanik, U. J., Rajinikanth, V., & Karthik, K. A. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04369-5 · Cited by 113 · see citing works
An Ensemble of 2D Convolutional Neural Networks for Tumor Segmentation
Lyksborg, M., Puonti, O., Agn, M., & Larsen, R. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_17 · Cited by 104 · see citing works
Combining Generative Models for Multifocal Glioma Segmentation and Registration
Kwon, D., Shinohara, R. T., Akbari, H., & Davatzikos, C. A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10404-1_95 · Cited by 100 · see citing works
A Convolutional Neural Network Approach to Brain Tumor Segmentation
Havaei, M., Dutil, F., Pal, C., Larochelle, H., & Jodoin, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6_17 · Cited by 100 · see citing works
Jiang, J., Wu, Y., Huang, M., Yang, W., Chen, W., & Feng, Q. (2013) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2013.05.007 · Cited by 97 · see citing works
Devos, A., Simonetti, A. W., van der Graaf, M., Lukas, L., Suykens, J. A. K., Vanhamme, L., Buydens, L. M. C., Heerschap, A., & Huffel, S. V. (2005) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2004.12.007 · Cited by 90 · see citing works
Degree prediction of malignancy in brain glioma using support vector machines
Li, G., Yang, J., Ye, C., & Geng, D. (2005) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2004.11.003 · Cited by 80 · see citing works
Development of computer‐aided approach for brain tumor detection using random forest classifier
Anitha, R., & Raja, D. S. S. (2017) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22255 · Cited by 73 · see citing works
Lung Nodule Detection Using Polygon Approximation and Hybrid Features from CT Images
Naqi, S. M., Sharif, M., Yasmin, M., & Fernandes, S. L. (2017) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405613666170306114320 · Cited by 70 · see citing works
Self-citationDeep and Wide Multiscale Recursive Networks for Robust Image LabelingPreprint
Huang, G. B., & Jain, V. (2013) · International Conference on Learning Representations · doi:10.48550/arxiv.1310.0354 · Cited by 45 · see citing works
Brain Tumor Segmentation Using a Generative Model with an RBM Prior on Tumor Shape
Agn, M., Puonti, O., af Rosenschöld, P. M., Law, I. G., & Leemput, K. V. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6_15 · Cited by 45 · see citing works
Shannon’s Entropy and Watershed Algorithm Based Technique to Inspect Ischemic Stroke Wound
Rajinikanth, V., Thanaraj, K. P., Satapathy, S. C., Fernandes, S. L., & Dey, N. (2018) · Smart innovation, systems and technologies · doi:10.1007/978-981-13-1927-3_3 · Cited by 45 · see citing works
Improving MRI segmentation with probabilistic GHSOM and multiobjective optimization
Ortíz, A., Górriz, J. M., Ramı́rez, J., & Salas-Gonzalez, D. (2012) · Neurocomputing · doi:10.1016/j.neucom.2012.08.047 · Cited by 44 · see citing works
de Wolde, H., Pruim, J., Mastik, M. F., Koudstaal, J., & Molenaar, W. M. (1997) · PubMed · Cited by 42 · see citing works
Multi-fractal detrended texture feature for brain tumor classification
Reza, S. M. S., Mays, R., & Iftekharuddin, K. M. (2015) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2083596 · Cited by 42 · see citing works
Classification of brain tumors using MRI and MRS data
Wang, Q., Liacouras, E. K., Miranda, E., Kanamalla, U. S., & Megalooikonomou, V. (2007) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.713544 · Cited by 17 · see citing works
ISLES Challenge 2015: Automated Model-Based Segmentation of Ischemic Stroke in MR Images
Haeck, T., Maes, F., & Suetens, P. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-30858-6_21 · Cited by 17 · see citing works
Neurocritical Care for the Advanced Practice Clinician
White, J. L., & Sheth, K. N. (2017) · Springer eBooks · doi:10.1007/978-3-319-48669-7 · Cited by 10 · see citing works
Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier
Rufus, N. H. A., & Selvathi, D. (2017) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22258 · Cited by 10 · see citing works
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Brain tumor detection and classification using machine learning: a comprehensive survey
Amin, J., Sharif, M. I., Haldorai, A., Yasmin, M., & Nayak, R. S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00563-y · Cited by 511 · see citing works
Self-citationRanjbarzadeh, R., Caputo, A., Tırkolaee, E. B., Ghoushchi, S. J., & Bendechache, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106405 · Cited by 336 · see citing works
Khan, P., Kader, M. F., Islam, S. M. R., Rahman, A. B., Kamal, M. S., Toha, M. U., & Kwak, K. S. (2021) · IEEE Access · doi:10.1109/access.2021.3062484 · Cited by 294 · see citing works
Role of deep learning in brain tumor detection and classification (2015 to 2020): A review
Nazir, M., Shakil, S., & Khurshid, K. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2021.101940 · Cited by 293 · see citing works
Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning
Almufareh, M. F., Imran, M., Khan, A., Humayun, M., & Asim, M. F. (2024) · IEEE Access · doi:10.1109/access.2024.3359418 · Cited by 200 · see citing works
Jyothi, P., & Singh, A. R. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10245-x · Cited by 164 · see citing works
Brain Tumor Detection and Multi-Grade Segmentation Through Hybrid Caps-VGGNet Model
Jabbar, A., Naseem, S., Mahmood, T., Saba, T., Alamri, F. S., & Rehman, A. U. (2023) · IEEE Access · doi:10.1109/access.2023.3289224 · Cited by 150 · see citing works
An artificial intelligence framework and its bias for brain tumor segmentation: A narrative review
Das, S., Nayak, G. K., Saba, L., Kalra, M. K., Suri, J. S., & Saxena, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105273 · Cited by 131 · see citing works
RanMerFormer: Randomized vision transformer with token merging for brain tumor classification
Wang, J., Lu, S., Wang, S., & Zhang, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2023.127216 · Cited by 131 · see citing works
A Tour of Unsupervised Deep Learning for Medical Image Analysis
Raza, K., & Singh, N. K. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210127154257 · Cited by 122 · see citing works
Amin, J., Anjum, M. A., Sharif, M. I., Jabeen, S., Kadry, S., & Ger, P. M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/3236305 · Cited by 117 · see citing works
Self-citationSharif, M. I., Li, J., Amin, J., & Sharif, A. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00310-3 · Cited by 108 · see citing works
Self-citationNaeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
Multi-class disease detection using deep learning and human brain medical imaging
Yousaf, F., Iqbal, S., Fatima, N., Kousar, T., & Rahim, M. S. M. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104875 · Cited by 92 · see citing works
Demir, F., Akbulut, Y., Taşcı, B., & Demir, K. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104424 · Cited by 77 · see citing works
Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model
Kader, I. A. E., Xu, G., Zhang, S., Saminu, S., Javaid, I., Ahmad, I. S., & Kamhi, S. (2021) · Diagnostics · doi:10.3390/diagnostics11091589 · Cited by 84 · see citing works
Reyes, D., & Sánchez, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25468 · Cited by 94 · see citing works
Deb, D., & Roy, S. S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09810-9 · Cited by 82 · see citing works
Sharif, M. I., Li, J., Khan, M. A., Kadry, S., & Tariq, U. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07204-6 · Cited by 77 · see citing works
Self-citationBrain Tumor Classification and Detection Based DL Models: A Systematic Review
Neamah, K., Mohamed, F., Adnan, M. M., Saba, T., Bahaj, S. A., Kadhim, K. A., & Rehman, A. U. (2023) · IEEE Access · doi:10.1109/access.2023.3347545 · Cited by 74 · see citing works
A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases
Bayram, B., Kunduracıoğlu, İ., İnce, S., & Paçal, İ. (2025) · Neuroscience · doi:10.1016/j.neuroscience.2025.01.020 · Cited by 73 · see citing works
Rastogi, D., Johri, P., Donelli, M., Kadry, S., Khan, A. A., Espa, G., Feraco, P., & Kim, J. (2025) · Scientific Reports · doi:10.1038/s41598-024-84386-0 · Cited by 70 · see citing works
A Review on Computer Aided Diagnosis of Acute Brain Stroke
Inamdar, M. A., Raghavendra, U., Gudigar, A., Chakole, Y., Hegde, A., Menon, G. R., Barua, P. D., Palmer, E. E., Cheong, K. H., Chan, W. Y., et al. (2021) · Sensors · doi:10.3390/s21248507 · Cited by 64 · see citing works
Yunus, U., Amin, J., Sharif, M. I., Yasmin, M., Kadry, S., & Krishnamoorthy, S. (2022) · Life · doi:10.3390/life12081126 · Cited by 64 · see citing works
Self-citationComputer-Aided Early Melanoma Brain-Tumor Detection Using Deep-Learning Approach
Asad, R., Rehman, S. U., Imran, A., Li, J., Almuhaimeed, A., & Alzahrani, A. (2023) · Biomedicines · doi:10.3390/biomedicines11010184 · Cited by 71 · see citing works
Brain tumour detection using machine and deep learning: a systematic review
Rasool, N., & Bhat, J. I. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19333-2 · Cited by 64 · see citing works
Chhabra, M., Ravulakollu, K. K., Kumar, M., Sharma, A., & Nayyar, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07894-y · Cited by 63 · see citing works
Dynamic architecture based deep learning approach for glioblastoma brain tumor survival prediction
Wankhede, D. S., & Selvarani, R. (2022) · Neuroscience Informatics · doi:10.1016/j.neuri.2022.100062 · Cited by 61 · see citing works
Rastogi, D., Johri, P., Donelli, M., Kumar, L., Bindewari, S., Raghav, A., & Khatri, S. K. (2025) · Life · doi:10.3390/life15030327 · Cited by 57 · see citing works
Satushe, V., Vyas, V., Metkar, S. P., & Singh, D. P. (2025) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2025.105414 · Cited by 56 · see citing works
Zafar, M., Amin, J., Sharif, M., Anjum, M. A., Mallah, G. A., & Kadry, S. (2023) · Mathematics · doi:10.3390/math11020364 · Cited by 53 · see citing works
Self-citationA deep autoencoder approach for detection of brain tumor images
Nayak, D. R., Padhy, N., Mallick, P. K., & Singh, A. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108238 · Cited by 52 · see citing works
Satyanarayana, G., Naidu, P. A., Desanamukula, V. S., kumar, K. S., & Rao, B. C. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104395 · Cited by 47 · see citing works
Bashkandi, A. H., Sadoughi, K., Aflaki, F., Alkhazaleh, H. A., Mohammadi, H., & Jimenez, G. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104434 · Cited by 47 · see citing works
Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
Shaukat, N., Amin, J., Sharif, M. I., Sharif, M. I., Kadry, S., & Sevcik, L. (2023) · Applied Sciences · doi:10.3390/app13053108 · Cited by 45 · see citing works
Self-citationTransfer Learning in Brain Tumor Classification: Challenges, Opportunities, and Future Prospects
Anwar, R. W., Abrar, M. Y. B. T., & Ullah, F. (2023) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc58733.2023.10392830 · Cited by 46 · see citing works
Brain tumor classification: a novel approach integrating GLCM, LBP and composite features
Dheepak, G., Johnvictor, A. C., & Vaishali, D. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2023.1248452 · Cited by 43 · see citing works
A Comprehensive Survey on Federated Learning: Concept and Applications
Mahlool, D. H., & Alsalihi, M. H. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-2069-1_37 · Cited by 41 · see citing works
Seismic damage identification of high arch dams based on an unsupervised deep learning approach
Cao, X., Chen, L., Chen, J., Li, J., Lu, W., Liu, H., 柯敏勇, & Tang, Y. (2023) · Soil Dynamics and Earthquake Engineering · doi:10.1016/j.soildyn.2023.107834 · Cited by 39 · see citing works
A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications
Upreti, D., Yang, E., Kim, H., & Seo, C. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.048932 · Cited by 43 · see citing works
Amin, J., Anjum, M., Sharif, M., Kadry, S., Nadeem, A., & Ahmad, S. (2022) · Diagnostics · doi:10.3390/diagnostics12040823 · Cited by 38 · see citing works
Self-citationRetractedComputational methods for automated mitosis detection in histopathology images: A review
Mathew, T., Kini, J. R., & Rajan, J. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.11.005 · Cited by 37 · see citing works
Onaizah, A. N., Xia, Y., & Hussain, K. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.11.063 · Cited by 40 · see citing works
A Comprehensive Survey on Quantum Machine Learning and Possible Applications
Umer, M. J., & Sharif, M. I. (2022) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.315730 · Cited by 35 · see citing works
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Tandon, R., Agrawal, S., Rathore, N. P. S., Mishra, A. K., & Jain, S. K. (2024) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.18144 · Cited by 34 · see citing works
A hybrid feature fusion deep learning framework for multi-source medical image analysis
Cao, Q., & Cheng, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2024.103934 · Cited by 34 · see citing works
Mandle, A. K., Sahu, S. P., & Gupta, G. P. (2022) · International Journal of Software Science and Computational Intelligence · doi:10.4018/ijssci.304438 · Cited by 33 · see citing works
DSNN: A DenseNet-Based SNN for Explainable Brain Disease Classification
Zhu, Z., Lu, S., Wang, S., Górriz, J. M., & Zhang, Y. (2022) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2022.838822 · Cited by 32 · see citing works
Adaptive features selection and EDNN based brain image recognition on the internet of medical things
Vankdothu, R., & Hameed, M. A. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108338 · Cited by 31 · see citing works
Deepak, S., & Ameer, P. M. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22543 · Cited by 31 · see citing works
Shaukat, N., Amin, J., Sharif, M., Azam, F., Kadry, S., & Krishnamoorthy, S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091454 · Cited by 31 · see citing works
Self-citationBrain Tumor Detection and Classification Using a New Evolutionary Convolutional Neural NetworkPreprint
Dehkordi, A. A., Hashemi, M., Neshat, M., Mirjalili, A., & Sadiq, A. S. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4292650 · Cited by 31 · see citing works
Magnetic resonance imaging-based brain tumor image classification performance enhancement
Alemu, B. S., Feisso, S., Mohammed, E. A., & Salau, A. O. (2023) · Scientific African · doi:10.1016/j.sciaf.2023.e01963 · Cited by 28 · see citing works
The Cluster Analysis and Feature Selection: Perspective of Machine Learning and Image Processing
Behura, A. (2021) · doi:10.1002/9781119785620.ch10 · Cited by 28 · see citing works
Robust detection of ductile fracture by acoustic emission data-driven unsupervised learning
Kwon, J.-H., Nguyen‐Nhat, T., Tran, M. T., Lee, H. W., Joo, H. S., Rhee, K., Park, S.-S., Kim, D. W., Jeong, Y.-G., & Kim, D.-K. (2024) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2024.109420 · Cited by 27 · see citing works
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Dhole, N. V., & Dixit, V. V. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12162-1 · Cited by 27 · see citing works
RetractedRaghavaiah, P., & Varadarajan, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103571 · Cited by 27 · see citing works
Alalwan, N., Alwadain, A. S., Alzahrani, A. I., Al-Bayatti, A. H., Abozeid, A., & El-Aziz, R. M. A. (2024) · Alexandria Engineering Journal · doi:10.1016/j.aej.2024.06.080 · Cited by 23 · see citing works
The power of deep learning for intelligent tumor classification systems: A review
Chandni, Sachdeva, M., & Kushwaha, A. K. S. (2023) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2023.108586 · Cited by 24 · see citing works
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Ramzan, M., Raza, M., Sharif, M. I., & Kadry, S. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12091459 · Cited by 19 · see citing works
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Aboussaleh, I., Riffi, J., Mahraz, M. A., & Tairi, H. (2023) · Annals of Data Science · doi:10.1007/s40745-023-00480-6 · Cited by 22 · see citing works
[68Ga]Ga-FAPI PET/CT in brain tumors: comparison with [18F]F-FDG PET/CT
Liu, Y., Ding, H., Cao, J., Liu, G., Chen, Y., & Huang, Z. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1436009 · Cited by 22 · see citing works
Real-time brain tumour diagnoses using a novel lightweight deep learning model
Alnageeb, M. H. O., & Supriya, M. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110242 · Cited by 21 · see citing works
Gupta, V. K., & Bibhu, V. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13769-0 · Cited by 21 · see citing works
Khan, S., Nosheen, F., Naqvi, S. S. A., Jamil, H., Faseeh, M., Khan, M. A., & Kim, D. (2024) · IEEE Access · doi:10.1109/access.2024.3392572 · Cited by 21 · see citing works
Pikulkaew, K. (2023) · International Joint Conference on Computer Science and Software Engineering · doi:10.1109/jcsse58229.2023.10202020 · Cited by 20 · see citing works
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Gayathri, B., Vidyasagar, K. E. C., Mudigonda, M., & Saikia, M. J. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10209-0 · Cited by 22 · see citing works
A successive framework for brain tumor interpretation using Yolo variants
Priyadharshini, S., Bhoopalan, R., Manikandan, D., & Ramaswamy, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-13155-4 · Cited by 19 · see citing works
Stacked auto-encoders based visual features for speech/music classification
Kumar, A., Solanki, S. S., & Chandra, M. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118041 · Cited by 19 · see citing works
Detection of brain space-occupying lesions using quantum machine learning
Amin, J., Anjum, M. A., Gul, N., & Sharif, M. I. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08717-4 · Cited by 19 · see citing works
Self-citationDetection and classification of brain tumor using hybrid feature extraction technique
Singh, M., Shrimali, V., & Kumar, M. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-14088-0 · Cited by 18 · see citing works
Salman, M., Das, P. K., & Mohanty, S. K. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3476544 · Cited by 18 · see citing works
Alamdari, M. M. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111173 · Cited by 15 · see citing works
Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor
Ahmed, H., Dada, M. O., & Samaila, B. B. (2023) · Material Science & Engineering International Journal · doi:10.15406/mseij.2023.07.00224 · Cited by 15 · see citing works
Brain tumor classification by using a novel convolutional neural network structure
Polat, Ö., Dokur, Z., & Ölmez, T. (2022) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22763 · Cited by 15 · see citing works
Krishna, P. R., Prasad, V. V. K. D. V., & Battula, T. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-13994-7 · Cited by 15 · see citing works
Rajinikanth, V., Vincent, P. M. D. R., Gnanaprakasam, C. N., Srinivasan, K., & Chang, C. (2023) · Diagnostics · doi:10.3390/diagnostics13111832 · Cited by 14 · see citing works
Krishnapriya, S., & Karuna, Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1347363 · Cited by 14 · see citing works
Amin, J., Sharif, M. I., Anjum, M. A., Nam, Y., Kadry, S., & Taniar, D. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.014199 · Cited by 14 · see citing works
Self-citationFruits and Vegetable Diseases Recognition Using Convolutional Neural Networks
Amin, J., Anjum, M. A., Sharif, M. I., Kadry, S., & Nam, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018562 · Cited by 14 · see citing works
Self-citationA novel multi-class brain tumor classification method based on unsupervised PCANet features
Shahin, A. I., Aly, S., & Aly, W. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08281-x · Cited by 13 · see citing works
A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques
Kaur, J., & Kaur, P. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108910 · Cited by 12 · see citing works
Chhabra, M., Shukla, M. K., & Ravulakollu, K. K. (2021) · International Journal of Performability Engineering · doi:10.23940/ijpe.21.04.p6.379393 · Cited by 12 · see citing works
Superlative Feature Selection Based Image Classification Using Deep Learning in Medical Imaging
Humayun, M., Khalil, M. I., Alwakid, G. N., & Jhanjhi, N. Z. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/7028717 · Cited by 12 · see citing works
S, H. M., Sowjanya, Sharma, N., Abhishek, G., Shivani, G., & Kumar, P. P. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-07012-9_22 · Cited by 11 · see citing works
MEHW‐SVM multi‐kernel approach for improved brain tumour classification
Dheepak, G., Christaline, J. A., & Vaishali, D. (2023) · IET Image Processing · doi:10.1049/ipr2.12990 · Cited by 11 · see citing works
Zhu, J., Gu, C., Wei, L., Li, H., Jiang, R., & Sheykhahmad, F. R. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28062 · Cited by 10 · see citing works
RetractedNatarajan, S., Govindaraj, V., Zhang, Y., Rajasekaran, M. P., Balasubramanian, K., Karunanithi, K., & Ejaz, K. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103866 · Cited by 10 · see citing works
Menaouer, B., El-Houda, K. N., Zoulikha, D., Mohammed, S., & Matta, N. (2022) · International Journal of Software Innovation · doi:10.4018/ijsi.293269 · Cited by 9 · see citing works
Sood, T., Bhatia, R. K., & Khandnor, P. (2023) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405619666230217100130 · Cited by 9 · see citing works
Parshapa, P., & Rani, P. I. (2023) · International Journal on Recent and Innovation Trends in Computing and Communication · doi:10.17762/ijritcc.v11i3.6203 · Cited by 9 · see citing works
CNN for Elderly Wandering Prediction in Indoor Scenarios
Oliveira, R. F. C., Feres, R., Barreto, F., & Abreu, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01091-3 · Cited by 9 · see citing works
RETRACTED: PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors
Lu, S., Satapathy, S. C., Wang, S., & Zhang, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.765654 · Cited by 8 · see citing works
RetractedA Novel Framework For Brain Disease Classification Using Quantum Convolutional Neural Network
Chandra, S., Saxena, S., Kumar, S., Chaube, M. K., & Bodhey, N. K. (2022) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece57977.2022.10150851 · Cited by 8 · see citing works
Mutkule, P. R., Sable, N. P., Mahalle, P. N., & Shinde, G. R. (2023) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815179187123040007 · Cited by 8 · see citing works
Xiao, Y., Feng, Y., & Sakurai, K. (2024) · Computers · doi:10.3390/computers13100269 · Cited by 7 · see citing works
Amin, J., Gul, N., & Sharif, M. I. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11013-y · Cited by 7 · see citing works
Self-citationDehkordi, A. A., Neshat, M., Khosravian, A., Thilakaratne, M., Sadiq, A. S., & Mirjalili, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-02890-3 · Cited by 8 · see citing works
Artificial intelligence‐assisted theranostics for brain tumors: Advancements and future perspectives
Li, M., Zhang, L., Ullah, Z., Zhang, Y., Wei, S., Huang, L., & Guo, B. (2025) · Open Research (University of Surrey) · doi:10.1002/viw.20250119 · Cited by 8 · see citing works
Brain MRI Segmentation and Tumor Detection: Challenges, Techniques and Applications
Ghorpade, N., & Bhapkar, H. R. (2021) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs51141.2021.9432346 · Cited by 7 · see citing works
- Authors
- Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M. A., Nisar, M. W., & Bukhari, S. A. C.
- Affiliations
- COMSATS University Islamabad (PK)
- Wah Medical College (PK)
- National University of Sciences and Technology (PK)
- St. John's University (US)
- Corresponding author
- Sharif, Muhammad
- Venue
- Journal of Medical Systems
- Published
- 2019-12-17
- Volume
- 44
- Issue
- 2
- Pages
- 32-32
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W2996632711
- Cited by
- 178
- References
- 61
- Citations by year
- 2026: 5 · 2025: 22 · 2024: 40 · 2023: 36 · 2022: 44 · 2021: 24 · 2020: 6 · 2019: 1
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor detection, stacked sparse autoencoder, MRI segmentation, image fusion, high-pass filter, median filter, BRATS dataset, tumor classification, medical image analysis
- MeSH terms
- Deep Learning, Algorithms, Brain Neoplasms, Diagnosis, Computer-Assisted, Humans, Image Processing, Computer-Assisted
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