Abstract
Abstract: The article suggests combining a cutting-edge deep convolution neural network (DCNN) with a texture map to automatically detect malignant spots and stain the ROI over the course of a single version. A better department dedicated to disease identification and a smaller department for linguistic segmentation and ROI marking make up the two cooperating branches of the anticipated DCNN version. The community version extracts the malignant spots with the help of the better department, and the lower department also gives the disease and heath careareas more accuracy. The community version pulls the textureimages from the input image in orderto form the options insidethe disease areas extra regular. The same old texture image deviation values are then decoded using a window. The best deviation values are then utilized to create a texture map that is divided into multiple patches and used as the computer's input for the deep convolution community version. The strategy proposed in this paper is known as a texture-map- based entirely department-collaborative network
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