Optimizing Clinical Decision-Making through the Integration of MobileNetV2 and Explainable AI in a Diabetic Retinopathy Screening System

Abstract

Diabetic Retinopathy is a severe diabetic complication and a leading cause of global blindness. While recent studies predominantly rely on computationally heavy deep learning models that evaluate images in isolation without considering patient clinical histories, their implementation in primary healthcare is frequently hindered by massive hardware demands and the inherent opacity of the algorithms. Addressing this research gap, this study aims to develop an efficient and interpretable hybrid Clinical Decision Support System (CDSS) for Diabetic Retinopathy detection. The proposed methodology integrates a lightweight Convolutional Neural Network architecture, MobileNetV2, to extract pathological features from retinal fundus images. To mitigate the limitations of purely image-centric models, the network's probability outputs are dynamically calibrated utilizing a Rule-Based algorithm that processes real-world patient medical parameters, specifically age and the duration of the diabetic history. Furthermore, the system incorporates Grad-CAM-based Explainable Artificial Intelligence (XAI) to generate real-time spatial mappings of retinal damage. The results demonstrate that MobileNetV2 achieves stable computational convergence while maintaining a highly optimal Sensitivity (Recall) rate. The hybrid mechanism successfully improves diagnostic decisions by contextually adjusting risk weights according to the patient's medical history. Additionally, the XAI evaluation through spatial heatmap projections accurately localizes retinal lesions, offering valid anatomical justification for clinical practitioners. In conclusion, the comprehensive integration of lightweight computational efficiency, clinical parametric logic, and analytical transparency produces a robust, hardware-friendly screening platform that is highly applicable for accelerating patient triage in primary healthcare facilities.