Cited by 305Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

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

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Sharif, Muhammad Imran, Khan, Muhammad Attique, Alhussein, Musaed, Aurangzeb, Khursheed, Raza, Mudassar·2022·Complex & Intelligent Systems

DOI 10.1007/s40747-021-00321-0

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Abstract

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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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: Sharif, Muhammad Imran
Khan, Muhammad Attique
Alhussein, Musaed
Aurangzeb, Khursheed
Raza, Mudassar
Venue: Complex & Intelligent Systems
Publisher: Springer Science and Business Media LLC
Published: 2021-03-09
Volume: 8
Issue: 4
Pages: 3007-3020
ISSN: 2198-6053 (ISSN-L)
2199-4536
DOI: 10.1007/s40747-021-00321-0
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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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A decision support system for multimodal brain tumor classification using deep learning

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