Detection Of CT Kidney Disease Using GLCM Feature Extraction and Kernel Extreme Learning Machine (KELM) Classification Method

Abstract

Kidney disease includes a variety of disorders affecting renal function, such as kidney cysts, tumors, and stones. If left untreated, these conditions can progress to chronic kidney disease, posing significant health risks and potentially leading to mortality. This study aims to classify kidney diseases by using the Gray Level Co-occurrence Matrix (GLCM) for feature extraction and the Kernel Extreme Learning Machine (KELM) as a classification method, with renal CT images as the dataset. The classification process categorizes kidney conditions into four classes: Cyst, Normal, Stone, and Tumor. The dataset consists of 4,232 CT images, with 1,058 images per class, evenly divided into axial and coronal orientations. The study utilizes k-fold cross-validation with k = 5 and k = 10 and implements the Radial Basis Function (RBF) as the kernel function in the KELM model. An iterative tuning of parameters, including the kernel parameter () and the regularization constant (), was conducted to identify the optimal model configuration. The best classification performance was achieved at angle using k = 5, with an accuracy of 97.26%, sensitivity of 97.16%, and specificity of 99.05%. Furthermore, the model demonstrated high computational efficiency, requiring only 6.07 seconds.