Brain Image Classification Using Time Frequency Extraction with Histogram Intensity Similarity

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 have also been developed, most of which are problem-distinct and shown to be highly favorable images. However, intensity-specific images not extracted. The recent deep learning ensure efficient means design end-to-end model that produces final accuracy with brain images, compromising normalization. To solve these problems, this paper, Histogram Time-frequency Differential Deep (HTF-DD) method for using Magnetic Resonance Image (MRI) presented. construction proposed involves following steps. First, a Convolutional Neural Network (CNN) trained as pooled feature mapping supervised manner result it obtains standardized intensified pre-processed features extraction. Second, set time-frequency extracted based time signal frequency obtain maps. Finally, Learning designed obtaining different classes. evaluated National Biomedical Imaging Archive (NBIA) validation computational time, overhead varied MRI has done.