Published Paper Details:

Kidney disease poses a significant health challenge, with kidney stones being a common manifestation that requires timely diagnosis and treatment. Traditional diagnostic methods often rely on radiological imaging, which can be time-consuming and subjective, heavily reliant on the expertise of radiologists. This research introduces a novel approach to automate the detection of kidney stones using deep learning techniques. In this study, a diverse dataset of medical images, encompassing normal kidney tissue, kidney stones, cysts, and tumors, is collected and annotated. Two state-of-the-art deep learning architectures, VGG16 and ResNet50, are employed for the classification task, leveraging their ability to extract intricate details from medical images. The methodology involves data preprocessing, including dataset balancing to prevent class imbalance bias. Feature selection techniques are applied to improve classification accuracy by removing irrelevant and redundant attributes. Convolutional Neural Networks (CNNs) are utilized, comprising convolution, pooling, and fully connected layers, to identify patterns in the images and classify them into the relevant categories. Results demonstrate the efficacy of the proposed deep learning models, achieving an impressive accuracy rate of 99.36% in the classification of kidney stones, cysts, tumors, and normal kidney tissue. Confusion matrix analysis further validates the model's accuracy. The outcomes of this research indicate that deep learning models can significantly expedite the diagnostic process for kidney disease, reduce healthcare costs, and potentially lead to earlier diagnoses and treatments. Moreover, the burden on radiologists can be alleviated through the deployment of a fully developed automated system for kidney illness detection.