Authors: Bhavana N, Kumar Siddamallappa. U, Neelamma. G, Anusha Jajur. J
Abstract: Kidney disease is a significant health concern worldwide, and identifying renal abnormalities at an early stage is essential for preventing disease progression and improving treatment outcomes. Manual interpretation of medical images can be time-consuming and may vary depending on clinical expertise. To address this challenge, this study presents a convolutional neural network (CNN)-based kidney disease classification system that incorporates transfer learning with ResNet101 and VGG16 architectures. The proposed model is trained and evaluated using an augmented dataset of renal ultrasound and CT images categorized into four classes: normal, cyst, stone, and tumor. Image preprocessing and data augmentation techniques are applied to improve image quality, increase dataset diversity, and enhance the model's generalization capability. By utilizing pre-trained deep learning models, the system effectively extracts meaningful image features while reducing training time and computational complexity. Experimental evaluation shows that ResNet101 achieves a classification accuracy of 97.4% while VGG16 achieves 95.8 %. Performance assessment using precision, recall, and F1-score further confirms the reliability of the proposed approach for multi-class kidney disease classification. The developed framework demonstrates the effectiveness of transfer learning for medical image analysis, particularly when labeled datasets are limited. In addition, the system has the potential to support radiologists by providing faster and more consistent diagnostic assistance, leading to improved clinical decision-making. Overall, the proposed approach ResNet101 offers an efficient and accurate solution for automated kidney disease detection and classification using deep learning techniques.
DOI: http://doi.org/10.5281/zenodo.21453407
