The texture is identifiable in optical and easy ways. Texture classification is an imperative region in texture analysis, where it gives descriptors for classifying the images. The categorization of normal and abnormal matter by magnetic resonance (MR), computed tomography (CT), and texture images has made noteworthy evolution in modern years. Recently, different novel robust classification techniques have been introduced to classify the different kinds of images for prediction. However, the accuracy of classification was not improved with lesser time. To address these issues, the edge‐preserved Tversky indexive Hellinger and deep perceptive Czekanowski classifier (ETIH‐DPCC) technique is introduced to segment and classify the images with more accuracy. The ETIH‐DPCC technique includes diverse processes namely preprocessing, segmentation, feature extraction, as well as classification. At first, different types of images, such as magnetic resonance imaging, CT, and texture, are used as input. With the acquired input, edge‐preserving normalized adaptive bilateral filtering is employed to carry the image preprocessing. In this stage, the noisy pixels are removed and edges are preserved. Then, the Tversky‐indexed quantile regression is applied to segment the images into diverse texture regions. After that, the feature extraction is performed on the segmented region using Hellinger kernel feature extraction, where a more informative feature for image prediction is extracted. During this process, the irrelevant features are avoided to decrease the dimensionality and feature extraction time. These extracted features are finally classified into positive and negative classes for disease prediction using DPCC. DPCC comprises multiple layers to deeply analyze the association between training and testing features. With this, the prediction accuracy is improved. Experimental outcomes show that the ETIH‐DPCC technique efficiently enhances prediction accuracy and less time compared to conventional methods.
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