A Deep Learning Model for Automated Kidney Disease Diagnosis Using CT Scan Images

Kidney diseases, including conditions such as cysts, tumors, stones and general cases, pose a major challenge to accurate and timely diagnosis, affect millions of people and place a significant burden on healthcare systems. Early and accurate diagnosis is central to effective disease management and patient outcomes. Diagnosing kidney disease traditionally involves a combination of clinical evaluations, laboratory tests, and medical imaging, and health professionals rely on a comprehensive understanding of the patient's history and symptoms. Recent advances in medical imaging, especially computed tomography (CT), have opened up possibilities for accurate and detailed imaging of the kidney. To address this, we propose a deep learning approach utilizing the VGG-19 and DenseNet-201 models for automated kidney disease diagnosis. The main goal is to develop a robust and efficient automated system that can detect kidney diseases and accurately diagnose it using CT image-focused deep learning. Our study demonstrates the efficacy of Convolutional Neural Networks (CNNs) in accurately detecting kidney disease with high precision. This approach holds promise for early detection and intervention, ultimately improving patient outcomes and public health outcomes.