Thangavel Renukadevi1,*, Kuppusamy Saraswathi1, P. Prabu2, K. Venkatachalam3
Computer Systems Science and Engineering, Vol.41, No.2, pp. 645-460, 2022, DOI:10.32604/csse.2022.020810
Abstract Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image… More >
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