AN APPROACH TOWARDS DIABETIC RETINOPATHY DETECTION AND ANALYSIS THROUGH COGNITIVE COMPUTING

Diabetes is a common chronic condition that significantly impacts patients' daily lives. Although it cannot be cured, if left unmanaged, diabetes can progressively damage vital organs. Without early and appropriate care, it may lead to multiple adverse effects. To ensure proper care, diabetic individuals typically require regular visits to healthcare professionals. This study proposes a predictive method that empowers diabetic individuals to monitor and manage their blood sugar levels without frequent doctor visits. The central objective of the proposed approach is to reduce the dependence on physician consultations and diagnostic center appointments. To analyze diabetic retinopathy datasets, the proposed system employs Deep Predictive Neural Networks (DPNNs). Retinal lesions are identified using the Region Convergence Algorithm (RCA), and features are extracted using the Strong Intensity Extractor (SIE), which captures significant pixel-level information. Cognitive Computing (CC), integrated with DPNN, is applied to optimize classification accuracy. The model's performance is evaluated using metrics such as Accuracy, Precision, Recall, and the Confusion Matrix. Numerous experimental inputs are provided to the system based on the developed model to verify and predict potential abnormalities.