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• W3164400994 abstract "In this era of rapid technological advancement, computer vision in medical analysis and diagnosis has a profound significance in detecting and classifying anomalies or diseases in the human body. This study proposes an ensemble learning method to classify brain tumors or neoplasms (i.e. glioma, meningioma, pituitary adenoma) and auto-immune disease lesion (i.e. multiple sclerosis) using magnetic resonance imaging (MRI) of brain tumors and multiple sclerosis patients. The method includes pre-processing, feature extraction, feature selection, and classification. The pre-processing phase uses region of interest (ROI) of both tumor and lesion, Collewet normalization, and Lloyd-max quantization. The base learner is designed with a support vector machine (SVM) classifier and prediction model with majority voting. Our proposed system operates well in contrast to other state-of-the-art methods, with the weighted sensitivity, specificity, precision, and accuracy of 97.5%, 98.838%, 98.011%, and 98.719%, respectively. Experimental results also show that the overall training and testing accuracy of our proposed model are 97.957% and 97.744% respectively. The findings may infer an unprecedented step for detecting the presence of lesions coexisting with the tumors in neuro-medicine diagnosis." @default.
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• W3164400994 date "2021-01-01" @default.
• W3164400994 modified "2023-10-01" @default.
• W3164400994 title "Classification of brain tumors and auto-immune disease using ensemble learning" @default.
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• W3164400994 doi "https://doi.org/10.1016/j.imu.2021.100608" @default.
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