An Automated Deep Learning Approach for Kidney Disease Detection

Chronic kidney disease (CKD) may cause significant damage to a person’s kidneys or lead to renal failure, which can be deadly. Kidney stones are a common symptom and may lead to serious conditions including kidney tumors and cysts. The timely and precise diagnosis of kidney illnesses is a major problem in modern healthcare, and this work aims to help solve that problem. This research suggests a deep-learning strategy for categorizing normal and various kidney diseases. using the custom convolutional neural network architecture. This study improves model performance through thorough validation and hyperparameter adjustment. Result findings show that the custom CNN model correctly differentiates between several kidney diseases with a validation accuracy of 0.9962, precision of 0.9925, and a validation loss of 0.0158. The technique used presents a new perspective on the problem space by capitalizing on current developments in deep learning, image processing, and predictive modeling.