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A decision support system for multimodal brain tumor classification using deep learning
Open accessAbstract
Multiclass classification of brain tumors is an important area of research in the field of medical imaging. Since accuracy is crucial in the classification, a number of techniques are introduced by computer vision researchers; however, they still face the issue of low accuracy. In this article, a new automated deep learning method is proposed for the classification of multiclass brain tumors. To realize the proposed method, the Densenet201 Pre-Trained Deep Learning Model is fine-tuned and later trained using a deep transfer of imbalanced data learning. The features of the trained model are extracted from the average pool layer, which represents the very deep information of each type of tumor. However, the characteristics of this layer are not sufficient for a precise classification; therefore, two techniques for the selection of features are proposed. The first technique is Entropy–Kurtosis-based High Feature Values (EKbHFV) and the second technique is a modified genetic algorithm (MGA) based on metaheuristics. The selected features of the GA are further refined by the proposed new threshold function. Finally, both EKbHFV and MGA-based features are fused using a non-redundant serial-based approach and classified using a multiclass SVM cubic classifier. For the experimental process, two datasets, including BRATS2018 and BRATS2019, are used without increase and have achieved an accuracy of more than 95%. The precise comparison of the proposed method with other neural nets shows the significance of this work.
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“These datasets vary in the number of images [55].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [55]
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
(2014) · Cited by 33,419
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
A Comprehensive Survey on Transfer Learning
Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., & He, Q. (2021) · Proceedings of the IEEE · doi:10.1109/jproc.2020.3004555 · Cited by 6,678
Bakas, S., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., Shinohara, R. T., Berger, C., Ha, S. M., Rozycki, M., et al. (2018) · Apollo - University of Cambridge Repository · doi:10.17863/cam.38755 · Cited by 1,441
A review of feature selection methods in medical applications
Remeseiro, B., & Bolón‐Canedo, V. (2019) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2019.103375 · Cited by 851
A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
Rehman, A., Naz, S., Razzak, I., Akram, F., & Imran, M. A. (2019) · Circuits Systems and Signal Processing · doi:10.1007/s00034-019-01246-3 · Cited by 717
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
Self-citationKhan, M. A., Ashraf, I., Alhaisoni, M. M., Damaševičius, R., Scherer, R., Rehman, A. U., & Bukhari, S. A. C. (2020) · Diagnostics · doi:10.3390/diagnostics10080565 · Cited by 436
Self-citationDeep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification
Mzoughi, H., Njeh, I., Wali, A., Slima, M. B., Hamida, A. B., Mhiri, C., & Mahfoudhe, K. B. (2020) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-020-00347-9 · Cited by 385
Acharya, U. R., Fernandes, S. L., WeiKoh, J. E., Ciaccio, E. J., Fabell, M. K. B. M., Tanik, U. J., Rajinikanth, V., & Yeong, C. H. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1428-9 · Cited by 328
Sharif, M. I., Li, J., Khan, M. A., & Saleem, M. A. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.019 · Cited by 328
Self-citationBig 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
Self-citationMicroscopic brain tumor detection and classification using 3D CNN and feature selection architecture
Rehman, A. U., Khan, M. A., Saba, T., Mehmood, Z., Tariq, U., & Ayesha, N. (2020) · Microscopy Research and Technique · doi:10.1002/jemt.23597 · Cited by 317
Self-citationAdvanced MR Imaging Techniques in the Diagnosis of Intraaxial Brain Tumors in Adults
Alokaili, R. N., Krejza, J., Wang, S., Woo, J. H., & Melhem, E. R. (2006) · Radiographics · doi:10.1148/rg.26si065513 · Cited by 275
Deep learning based enhanced tumor segmentation approach for MR brain images
Mittal, M., Goyal, L. M., Kaur, S., Kaur, I., Verma, A., & Hemanth, D. J. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.02.036 · Cited by 275
A Comprehensive Survey on Transfer LearningPreprint
Zhuang, F., Qi, Z., Duan, K., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02685 · Cited by 223
Ghaffari, M., Sowmya, A., & Oliver, R. (2019) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2019.2946868 · Cited by 214
Sharif, M., Amin, J., Raza, M., Yasmin, M., & Satapathy, S. C. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.017 · Cited by 206
Self-citationKhan, M. A., Lali, M. I. U., Rehman, A. U., Ishaq, M., Sharif, M. I., Saba, T., Zahoor, S., & Akram, T. (2019) · Microscopy Research and Technique · doi:10.1002/jemt.23238 · Cited by 186
Self-citationKhan, M. A., Zhang, Y., Sharif, M., & Akram, T. (2021) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2020.106956 · Cited by 173
Self-citationGastrointestinal diseases segmentation and classification based on duo-deep architectures
Khan, M. A., Khan, M. A., Ahmed, F., Mittal, M., Goyal, L. M., Hemanth, D. J., & Satapathy, S. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.12.024 · Cited by 168
Self-citationPrimary Brain Tumors in Adults: Diagnosis and Treatment.
Perkins, A., & Liu, G. (2016) · PubMed · Cited by 162
Contrast enhanced medical MRI evaluation using Tsallis entropy and region growing segmentation
Raja, N. S. M., Fernandes, S. L., Dey, N., Satapathy, S. C., & Rajinikanth, V. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0854-8 · Cited by 162
Sharif, M. I., Tanvir, U., Munir, E. U., Khan, M. A., & Yasmin, M. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-1075-x · Cited by 157
Self-citationDeveloped Newton-Raphson based deep features selection framework for skin lesion recognition
Khan, M. A., Sharif, M. I., Akram, T., Bukhari, S. A. C., & Nayak, R. S. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.034 · Cited by 152
Self-citationKhan, M. A., Rubab, S., Kashif, A., Sharif, M. I., Muhammad, N., Shah, J. H., Zhang, Y., & Satapathy, S. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.11.014 · Cited by 148
Self-citationAmin, J., Sharif, M., Yasmin, M., Saba, T., Anjum, M. A., & Fernandes, S. L. (2019) · Journal of Medical Systems · doi:10.1007/s10916-019-1453-8 · Cited by 147
Self-citationA hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images
Narmatha, C., Eljack, S. M., Tuka, A. A. R. M., Manimurugan, S., & Mustafa, M. E. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02470-5 · Cited by 141
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
Self-citationFernandes, 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
Chemotherapy for Brain Tumors — A New Beginning
DeAngelis, L. M. (2005) · New England Journal of Medicine · doi:10.1056/nejme058010 · Cited by 101
StomachNet: Optimal Deep Learning Features Fusion for Stomach Abnormalities Classification
Khan, M. A., Sarfraz, M. S., Alhaisoni, M. M., Albesher, A., Wang, S., & Ashraf, I. (2020) · IEEE Access · doi:10.1109/access.2020.3034217 · Cited by 100
Self-citationAppearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning
Raza, M., Sharif, M., Yasmin, M., Khan, M. A., Saba, T., & Fernandes, S. L. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.05.002 · Cited by 99
Self-citationA Hybrid Framework to Evaluate Breast Abnormality Using Infrared Thermal Images
Fernandes, S. L., Rajinikanth, V., & Kadry, S. (2019) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2019.2923926 · Cited by 91
Hussain, N., Khan, M. A., Sharif, M. I., Khan, S. A., Albesher, A., Saba, T., & Armghan, A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08852-3 · Cited by 91
Self-citationSegmentation and Analysis of Brain Tumor Using Tsallis Entropy and Regularised Level Set
Rajinikanth, V., Fernandes, S. L., Bhushan, B., Harisha, A., & Sunder, N. R. (2017) · Lecture notes in electrical engineering · doi:10.1007/978-981-10-4280-5_33 · Cited by 88
Issues of Diagnostic Review in Brain Tumor Studies: From the Brain Tumor Epidemiology Consortium
Davis, F. G., Malmer, B., Aldape, K. D., Barnholtz‐Sloan, J. S., Bondy, M. L., Brännström, T., Bruner, J. M., Burger, P. C., Collins, V. P., Inskip, P. D., et al. (2008) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-07-0725 · Cited by 71
Diabetic Retinopathy Diagnosis from Retinal Images Using Modified Hopfield Neural Network
Hemanth, D. J., Anitha, J., Sơn, L. H., & Mittal, M. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1111-6 · Cited by 71
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
Self-citationImaging biomarkers of angiogenesis and the microvascular environment in cerebral tumours
Thompson, G., Mills, S. J., Coope, D. J., O’Connor, J. P. B., & Jackson, A. (2011) · British Journal of Radiology · doi:10.1259/bjr/66316279 · Cited by 61
Huang, W., Xue, Y., & Wu, Y. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219369 · Cited by 58
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
Khan, M. A., Qasim, M., Lodhi, H. M. J., Nazir, M., Javed, K., Rubab, S., Din, A., & Habib, U. (2020) · Microscopy Research and Technique · doi:10.1002/jemt.23578 · Cited by 45
Self-citationCognitive outcome after radiotherapy in brain tumor
Durand, T., Bernier, M., Léger, I., Taillia, H., Noël, G., Psimaras, D., & Ricard, D. (2015) · Current Opinion in Oncology · doi:10.1097/cco.0000000000000227 · Cited by 43
Nazar, U., Khan, M. A., Lali, I. U., Javed, K., Ashraf, I., Tariq, J., Ali, H., & Din, A. (2020) · Journal of Control Engineering and Applied Informatics · Cited by 37
Self-citationReview of Automated Computerized Methods for Brain Tumor Segmentation and Classification
Nazar, U., Khan, M. A., Lali, I. U., Lin, H., Ali, H., Ashraf, I., & Tariq, J. (2019) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405615666191120110855 · Cited by 31
Self-citation
Research works printed but not linked: 1
- Nazir M, Khan MA, Saba T, Rehman A (2019) Brain tumor detection from MRI images using multilevel wavelets. Int Conf Comput Inf Sci (ICCIS) 2019:1–5
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Self-citationA deep learning approach for brain tumor classification using MRI images
Aamir, M., Rahman, Z.-U., Dayo, Z. A., Abro, W. A., Uddin, M. I., Khan, I. U., Imran, A. S., Ali, Z., Ishfaq, M., Guan, Y., et al. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108105 · Cited by 338
Jabeen, K., Khan, M. A., Alhaisoni, M., Tariq, U., Zhang, Y.-D., Hamza, A., Mickus, A., & Damaševičius, R. (2022) · Sensors · doi:10.3390/s22030807 · Cited by 314
Self-citationA Survey of Brain Tumor Segmentation and Classification Algorithms
Biratu, E. S., Schwenker, F., Ayano, Y. M., & Debelee, T. G. (2021) · Journal of Imaging · doi:10.3390/jimaging7090179 · Cited by 202
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Metadata
- Authors
- Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M.
- Affiliations
- COMSATS University Islamabad (PK)
- HITEC University (PK)
- King Saud University (SA)
- Corresponding author
- Khan, Muhammad Attique
- ORCID
- Venue
- Complex & Intelligent Systems
- Published
- 2021-03-09
- Volume
- 8
- Issue
- 4
- Pages
- 3007-3020
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- OpenAlex
- W3134269260
- Cited by
- 305
- References
- 49
- Citations by year
- 2026: 29 · 2025: 81 · 2024: 68 · 2023: 58 · 2022: 46 · 2021: 23
- FWCI
- 19.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- Funders
- Deanship of Scientific Research, King Saud University (RG-1438-034)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Keywords
- brain tumor classification, multimodal medical imaging, decision support system, transfer learning, class imbalance, feature selection, improved genetic algorithm, threshold functions, feature fusion, multi-class SVM, BraTS 2018, BraTS 2019
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