Fast Accurate Detection and Classification of Kidney Diseases from CT Images using Hybrid Classifiers

This research introduces an innovative method of Artificial Intelligence (AI) for improving the detection and classification of kidneydiseases using CT images. The proposed method includes image preprocessing to remove artifacts, noise, and other image quality issuesthat can affect the accuracy of diagnosis. Then the area of interest in each image is segmented using Fractional Darwinian particleswarm optimization. Different features including Local Binary Pattern, Hu Moments, and Gray level zone length matrix (GLZLM) areextracted and fused using Canonical Correlation Analysis (CCA) andreduced using Two Dimensional Principal Component Analysis (2DPCA) to maintain only dominant features. A two-level classificationapproach is carried out to provide both fast and detailed diagnosisusing both Binary Support Vector Machine (BSVM) and ConvolutionalNeural Network (CNN) in sequence. BSVM is used to initially discriminate between normal and kidney diseases categories. Afterwards, thedetected abnormal kidney images are classified using CNN to different kidney diseases such as stones, cysts, and tumors. This approachaims to expedite the diagnostic procedure while also enhancing the efficiency and accuracy of classifying kidney disease in the clinical practice.Obtained results validate the efficiency of our proposed in terms ofachieved accuracy when compared to alternative cutting-edge methods.