Enhanced Chronic Kidney Disease Detection Using Deep Learning: A Comparative Analysis of CNN and LSTM Models.

Chronic Kidney Disease (CKD) is a degenerative disorder that offers a huge worldwide health threat, frequently resulting in severe complications if not recognized early. Traditional diagnostic methods can be time-consuming, resource-intensive, and subject to human error. With the growth of artificial intelligence in healthcare, deep learning algorithms have emerged as strong tools for accurately and efficiently detecting CKD. This study compares two major deep learning model Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) for the early detection and categorization of CKD. The models were tested with and without the Synthetic Minority Over-sampling Technique (SMOTE) to resolve data imbalances. Performance criteria such as accuracy, precision, recall, and F1-score were employed for evaluation. CNN with SMOTE had the best performance, with an accuracy of 99%, precision of 99%. In contrast, LSTM with SMOTE had 91% accuracy,89% precision. Also table highlights overall model performance, and shows class wise accuracy for detecting Normal, Cyst, Stone, and Tumor instances, with CNN with SMOTE outperforming LSTM with SMOTE in all classes. Our data demonstrate the efficacy of CNN, particularly when paired with SMOTE, in reaching high diagnostic accuracy.