An efficient optimized probabilistic neural network based kidney stone detection and segmentation over ultrasound images

Abstract

Locating renal calculus in the ultrasound image is a demanding requirement in the field of medical imaging. For accurate detection of kidney stone, in this paper, optimal recurrent neural network (OPNN) is adopted. The proposed work undergoes pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in input images is removed with the median filter because noises impact the accuracy of the classification. Then, compute features of this image. In the classification stage, features are used to classify defects through optimal probabilistic NeuralNetwork (OPNN). OPNN is a combination of PNN and spider monkey optimization (SMO). The parameter of PNN is optimized with the help of SMO. Then, the stone region from the abnormal image is segmented using probabilistic fuzzy c-means clustering (PFCM). The proposed methodology performance can be analyzed by using Sensitivity, Accuracy, and Specificity.