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

doi:10.1007/s12204-023-2625-8

Abstract

Abstract: 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.

Key words: brain tumor, magnetic resonance imaging, deep learning, deep-convolutional neural network classifier, LOBO optimization

摘要： 脑肿瘤分类是医疗保健应用中的一个重要问题。精确与及时的脑肿瘤识别对于有效地治疗这种疾病是重要的。脑肿瘤在大小、形状和数量上都有很大的变化，因此分类过程是一个很困难的研究问题。本文提出了一种利用磁共振成像技术的深度学习模型，克服了现有分类方法的局限性。所提方法的有效性取决于coyote优化算法，也称为LOBO算法，该算法对深度卷积神经网络分类器的权重进行优化。其准确度、灵敏度和特异度分别为92.401 1%、94.154 1%和91.928 6%，验证了该方法的有效性。结果表明，该方法对脑肿瘤可进行有效分类。

关键词: 脑肿瘤，磁共振成像，深度学习，深度卷积神经网络分类器，LOBO优化

CLC Number:

A. Sahaya Anselin Nisha^1* , NARMADHA R.¹ , AMIRTHALAKSHMI T. M.²,BALAMURUGAN V.¹, VEDANARAYANAN V.¹. LOBO Optimization-Tuned Deep-Convolutional Neural Network for Brain Tumor Classification Approach[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(1): 107-114.

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