Deep Learning Techniques for Chronic Kidney Disease Diagnosis

Chronic Kidney Disease (CKD) is characterized by a progressive loss of kidney function, which can ultimately result in kidney damage or failure. As the condition advances, it becomes increasingly challenging to diagnose. Some doctors are looking forward to advancements in technology that can scan a patient’s body and analyze their diseases. Leveraging data from routine clinical visits to assess different stages of CKD can facilitate early detection and timely treatment. Deep learning has the potential to make many things in the world seem possible, even causing surprise with its capabilities. In this study, we propose a deep learning-based framework for the diagnosis of CKD. Deep learning technology has opened up new possibilities in the medical field, often delivering results that exceed expectations. The dataset used was sourced from the Deep Learning Repository of the University of California, Irvine (UCI). Various deep learning algorithms, including Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNNs), MobileNet, and models specific to kidney disease, were implemented. Among these, the Random Forest classifier delivered the highest accuracy, highlighting the effectiveness of our method in segmenting medical images related to kidney and tumour analysis.