Enhancing Diagnostic Accuracy for Kidney Abnormalities Through Resolution-Optimized Deep Learning Models

The kidney is essential in human physiology, regulating fluid balance, filtering waste, and producing vital hormones. Accurate diagnosis of kidney issues is crucial, with CT scans and ultrasounds being indispensable tools for detecting renal diseases and abnormalities. Even with better imaging technology, it can still be difficult to spot kidney problems because they can be very subtle. Machine Learning (ML) and Deep Learning (DL) techniques can help find and classify kidney issues from medical images. These methods can look at complicated patterns in the images, helping doctors make better decisions. In this study, we propose Resolution-Optimized Deep Learning Models build for the detection of kidney abnormalities including cysts, tumors, and stones. We show that by optimizing the resolution of the images obtained from CT scans, the accuracy of abnormality detection can be significantly improved. Our findings underscore the importance of optimizing image resolution in improving diagnostic accuracy, thereby facilitating early identification and timely intervention for renal diseases.