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Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of $96.67\%$ with $100\%$ of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.

Ultrasound (US) imaging is the initial phase in the preliminary diagnosis for the treatment of kidney diseases, particularly to estimate kidney size, shape and position, to give information about kidney function, and to help in diagnosis of abnormalities like cysts, stones, junctional parenchyma and tumors which is shown in Figs. 7–9. This study proposes Grey Level Co-occurrence Matrix (GLCM)-based Probabilistic Principal Component Analysis (PPCA) and Artificial Neural Network (ANN) method for the classification of kidney images. Grey Wolf Optimization (GWO) is used to update the current positions of abnormal kidney images in the discrete searching space, thus getting the optimal feature subset for better classification purposes based on Feed Forward Neural Network (FFNN). The scanned image is pre-processed and the required features are extracted by GLCM, among those, some features are selected by PPCA. Feed Forward Back propagation Neural Network (FFBN) is used to classify the normalities and abnormalities in the part of kidney images. The proposed methodology is implemented in MATLAB platform and the analyzed result produces 98% accuracy using GWO-FFBN technique.