Kidney Stone Detection Using Image Processing and Convolutional Neural Networks

This research focuses on the development of an advanced system for kidney stone detection through the integration of image processing, Convolutional Neural Networks (CNNs) with the specifically chosen VGG16 architecture, and deep learning techniques. The utilization of VGG16 is motivated by its proven efficacy in image classification tasks due to its deep and well-structured architecture. The methodology incorporates pre-processing steps involving noise removal through Median filtering and enhancement via Adaptive Histogram Equalization (AHE). The classification phase employs the VGG16 CNN to categorize kidney images into normal and abnormal, with a subsequent innovation of delivering classification results through voice output. In cases of abnormal classification, the system further employs a robust approach for kidney stone detection using Fuzzy C-Means (FCM) clustering and level set segmentation. The accuracy of this comprehensive methodology is a critical aspect, contributing to the reliability and efficiency of the proposed system for automated kidney stone detection, which holds significant implications for timely and accurate medical diagnostics.

Keywords: Kidney Stones, Image Processing, CT image, CNN, Deep Learning Algorithm, Classification, Voice Output, Accuracy.