Abstract
Kidney stone diagnosis is one of the sensitive issues in personal healthcare. Detecting kidney stones early can play a vital role in avoiding chronic kidney diseases and related surgical procedures. However, due to several associated issues, identifying a kidney stone in the early stages can be very difficult. In this research, a classification model for automated diagnosis of kidney stones utilizing coronal computed tomography (CT) images is suggested. Due to low resolution and the presence of noise, every image is passed through an image enhancement step before feeding into a VGG-19 based CNN Model. The training dataset used contains 1799 cross-sectional CT scan images from 433 individuals. Data augmentation is carried out to avoid overfitting of the deep model. The developed model can correctly identify kidney stones of even tiny size with a 97.62% precision, 98.79% recall, and 98.62% accuracy. The developed model performs better than recent similar work and is suitable for e-healthcare systems. It demonstrates that such deep-learning-based techniques can be utilized to solve other similar issues in urology.
Authors
Abhisek Gour1, Vishal Suthar2
MBM University, India1, Government Polytechnic College Mandore, India2
Keywords
Kidney Stone Detection, Computational Tomography Images, Convolutional Neural Networks, VGG19 Model