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.
A. Sahaya Anselin Nisha1*
,
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]. Journal of Shanghai Jiaotong University(Science), 2025
, 30(1)
: 107
-114
.
DOI: 10.1007/s12204-023-2625-8
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