Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network

Abstract The prevalence of chronic kidney disease (CKD) increases annually in the present scenario of research. One of the sources for further therapy is the CKD prediction where the Machine learning techniques become more important in medical diagnosis due to their high accuracy classification ability. In the recent past, the accuracy of classification algorithms depends on the proper use of algorithms for feature selection to reduce the data size. In this paper, Heterogeneous Modified Artifical Neural Network (HMANN) has been proposed for the early detection, segmentation, and diagnosis of chronic renal failure on the Internet of Medical Things (IoMT) platform. Furthermore, the proposed HMANN is classified as a Support Vector Machine and Multilayer Perceptron (MLP) with a Backpropagation (BP) algorithm. The proposed algorithm works based on an ultrasound image which is denoted as a preprocessing step and the region of kidney interest is segmented in the ultrasound image. In kidney segmentation, the proposed HMANN method achieves high accuracy and significantly reducing the time to delineate the contour.