Deep Learning for the Classification of Kidney Diseases in Medical Images Using ResNet-50 and Grad-CAM

Abstract

Renal pathology represents a diverse set of diseases that present significant clinical relevance. Included among the various types of renal pathologies are renal stones, cysts, and renal malignancies, all of which require diagnosis and therapy to prevent progression of the disease process. The current research study was performed to create and validate a classification model based on deep learning using a convolutional neural networks (CNN) architecture, namely a 50-layer Residual Network (ResNet-50) using Gradient-weighted Class Activation Mapping (Grad-CAM), to provide improved automatic detection of renal pathology from medical images and improve the interpretability of those medical images. During the study, the Explainable Deep Learning Pipeline (X-DLP) paradigm was followed, which provides a structured methodology to perform research with the use of deep learning in medical imaging. The X-DLP structures the research process into a series of phases, including Data acquisition and curation, Preprocessing and Augmentation, Model Creation via Transfer Learning, and lastly, Interpretability and Visualization.The results obtained show that the proposed model performs consistently well across different evaluation metrics. The Precision–Recall curve, with a PR-AUC close to 0.89, suggests that the model is effective at identifying positive cases even when the data are imbalanced. In addition, the F1-score reaches a peak of around 0.835 at a threshold near 0.45, indicating a good trade-off between precision and recall. From another perspective, the evaluation using Youden’s criterion reveals sensitivity and specificity values close to 0.80, which supports the model’s ability to distinguish between classes with reasonable accuracy. Moreover, the lift and cumulative gain analysis further highlight its practical usefulness, with a lift of 3.5 in the top 10% and a cumulative gain of 75% when considering 30% of the population. These results indicate that the model can effectively prioritize the most relevant positive cases. Overall, these findings suggest that the model can serve as a valuable support tool in medical diagnosis. By enabling automated classification of renal images and providing visual insights through interpretability techniques, it helps streamline clinical decision-making, reduces reliance on purely manual assessments, and enhances its potential for real-world application.