LOBO Optimization-Tuned Deep-Convolutional Neural Network for Brain Tumor Classification Approach

• Published in: Shanghai jiao tong da xue xue bao Vol. 30; no. 1; pp. 107 - 114
• Main Authors: Nisha, A. Sahaya Anselin, Narmadha, R., Amirthalakshmi, T. M., Balamurugan, V., Vedanarayanan, V.
• Format: Journal Article
• Language: English
• Published: Shanghai Shanghai Jiaotong University Press 01.02.2025; Springer Nature B.V; School of Electrical and Electronics,Sathyabama Institute of Science and Technology,Chennai 600119,Tamil Nadu,India%Department of Electronics and Communication Engineering,SRM Institute of Science and Technology,Ramapuram Campus,Chennai 600089,Tamil Nadu,India
• Subjects: Algorithms; Architecture; Artificial neural networks; Brain; Brain tumors; Classification; Computer Science; Deep learning; Effectiveness; Electrical Engineering; Engineering; Imaging techniques; Life Sciences; Machine learning; Magnetic resonance imaging; Materials Science; Neural networks; Neuroimaging; Optimization; Tumors; A; magnetic resonance imaging; 脑肿瘤; deep-convolutional neural network classifier; 深度学习; brain tumor; TP 391; LOBO optimization; deep learning; 磁共振成像; 深度卷积神经网络分类器; LOBO优化; R 739.91
• ISSN: 1007-1172; 1674-8115; 1995-8188
• DOI: 10.1007/s12204-023-2625-8

The categorization of brain tumors is a significant issue for healthcare applications. Perfect and timely identification of brain tumors is important for employing an effective treatment of this disease. Brain tumors possess high changes in terms of size, shape, and amount, and hence the classification process acts as a more difficult research problem. This paper suggests a deep learning model using the magnetic resonance imaging technique that overcomes the limitations associated with the existing classification methods. The effectiveness of the suggested method depends on the coyote optimization algorithm, also known as the LOBO algorithm, which optimizes the weights of the deep-convolutional neural network classifier. The accuracy, sensitivity, and specificity indices, which are obtained to be 92.40%, 94.15%, and 91.92%, respectively, are used to validate the effectiveness of the suggested method. The result suggests that the suggested strategy is superior for effectively classifying brain tumors.