Chronic Kidney Disease Detection using Deep Learning Models

Chronic Kidney Disease (CKD) is a pervasive health challenge necessitating accurate and timely diagnosis to mitigate its progression and associated complications. This study proposes an innovative framework for CKD detection, integrating advanced deep learning methodologies with fuzzy logic principles to capitalize on both medical imaging data and longitudinal patient records. The model incorporates Convolutional Neural Networks (CNNs) for robust image analysis, recurrent neural networks (RNNs), including long short-term memory (LSTM) networks, for sequential data processing, And Fuzzy Hybrid Neural Networks (FHNNs) for integrating fuzzy logic with deep learning. By synergistically harnessing the capabilities of CNNs in extracting spatial features from medic15al images, RNNs in analysing temporal patterns from patient records, and FHNNs in incorporating fuzzy logic for uncertainty handling and interpretability, the proposed methodology aims to achieve unparalleled accuracy and robustness in CKD diagnosis. Additionally, the research introduces a novel evaluation metric, the Renal Impairment Index (RII), amalgamating multiple clinical parameters to provide a comprehensive assessment of CKD severity. Extensive experimentation on diverse datasets validates the efficacy and clinical relevance of the proposed methodology, demonstrating promising performance in early CKD detection &staging.