KidneyNet: A Novel CNN-Based Technique for the Automated Diagnosis of Chronic Kidney Diseases from CT scans

This study presents KidneyNet, an innovative computer-aided diagnosis (CAD) system
designed to identify chronic kidney diseases (CKDs), such as kidney stones, cysts, and tumors, in
CT scans. KidneyNet utilizes a convolutional neural network (CNN) structure consisting of eight
convolutional layers, three pooling layers, a flattening layer, and two fully connected layers. Small
filters enhance computational efficiency by reducing the number of parameters and minimizing the
risk of overfitting compared to larger filters. The model captures more complex and abstract features
as data move through the layers. The initial layers identify basic patterns, while the deeper layers
focus on more intricate representations. KidneyNet aims to enhance the efficiency and accuracy of
kidney disease diagnosis. Additionally, the model incorporates the gradient-weighted class activation
mapping (Grad-CAM) algorithm, which helps to pinpoint affected areas in the scans. This feature
improves interpretability, allowing clinicians to identify which regions the model deemed significant
for detecting abnormalities such as tumors, cysts, or stones. Through extensive testing on a CT
kidney dataset, KidneyNet demonstrated impressive performance metrics, with 99.88% accuracy,
99.92% specificity, 99.76% sensitivity, 99.58% precision, and an F1 score of 99.67%, outperforming
existing models. This approach alleviates the diagnostic burden on radiologists and promotes early
detection, potentially saving lives. This study highlights the critical role of advanced imaging
analysis in addressing kidney conditions and emphasizes KidneyNet’s capability to deliver precise
and cost-effective diagnoses.