Kidney diseases classification based on SONN and MLP-GA in ultrasound radiography images

A strategy for robust classification of renal ultrasound images for the identification of three kidney disorders, renal calculus, cortical cyst, and hydronephrosis, has been attempted. Features were retrieved using the intensity histogram (IH), grey level co-occurrence matrices (GLCMs), and grey level run length matrices (GLRLMs) techniques. Using the extracted features, input samples are created and then fed to a hybrid model which is a combination of self-organising neural network (SONN) and multilayer perceptron (MLP) trained with a genetic algorithm (GA). Self-organising neural network (SONN) is used to cluster the input patterns into four groups or clusters and finally, MLP using genetic algorithm is employed on each cluster to classify the input patterns. The proposed hybrid method using SONN and MLP-GA has more potential to classify the ultrasound images by achieving a precision of 93.9%, recall of 93.0%, F1 score of 93.0%, and overall accuracy of 96.8%.