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Article

Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists

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Department of Computer Science, HITEC University, Museum Road, Taxila 47080, Pakistan
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Department of Computer Engineering, HITEC University, Museum Road, Taxila 47080, Pakistan
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College of Computer Science and Engineering, University of Ha’il, Ha’il 81451, Saudi Arabia
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
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Department of Intelligent Computer Systems, Czestochowa University of Technology, 42-200 Czestochowa, Poland
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College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia
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Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, NY 11439, USA
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Author to whom correspondence should be addressed.
Diagnostics 2020, 10(8), 565; https://doi.org/10.3390/diagnostics10080565
Received: 18 June 2020 / Revised: 1 August 2020 / Accepted: 4 August 2020 / Published: 6 August 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Manual identification of brain tumors is an error-prone and tedious process for radiologists; therefore, it is crucial to adopt an automated system. The binary classification process, such as malignant or benign is relatively trivial; whereas, the multimodal brain tumors classification (T1, T2, T1CE, and Flair) is a challenging task for radiologists. Here, we present an automated multimodal classification method using deep learning for brain tumor type classification. The proposed method consists of five core steps. In the first step, the linear contrast stretching is employed using edge-based histogram equalization and discrete cosine transform (DCT). In the second step, deep learning feature extraction is performed. By utilizing transfer learning, two pre-trained convolutional neural network (CNN) models, namely VGG16 and VGG19, were used for feature extraction. In the third step, a correntropy-based joint learning approach was implemented along with the extreme learning machine (ELM) for the selection of best features. In the fourth step, the partial least square (PLS)-based robust covariant features were fused in one matrix. The combined matrix was fed to ELM for final classification. The proposed method was validated on the BraTS datasets and an accuracy of 97.8%, 96.9%, 92.5% for BraTs2015, BraTs2017, and BraTs2018, respectively, was achieved. View Full-Text
Keywords: brain tumor; healthcare; linear contrast; transfer learning; deep learning features; feature selection; feature fusion; PLS; ELM brain tumor; healthcare; linear contrast; transfer learning; deep learning features; feature selection; feature fusion; PLS; ELM
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MDPI and ACS Style

Khan, M.A.; Ashraf, I.; Alhaisoni, M.; Damaševičius, R.; Scherer, R.; Rehman, A.; Bukhari, S.A.C. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics 2020, 10, 565. https://doi.org/10.3390/diagnostics10080565

AMA Style

Khan MA, Ashraf I, Alhaisoni M, Damaševičius R, Scherer R, Rehman A, Bukhari SAC. Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists. Diagnostics. 2020; 10(8):565. https://doi.org/10.3390/diagnostics10080565

Chicago/Turabian Style

Khan, Muhammad Attique, Imran Ashraf, Majed Alhaisoni, Robertas Damaševičius, Rafal Scherer, Amjad Rehman, and Syed Ahmad Chan Bukhari. 2020. "Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists" Diagnostics 10, no. 8: 565. https://doi.org/10.3390/diagnostics10080565

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