The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
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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A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
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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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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
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
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 218
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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