Leveraging Advanced Image Processing Techniques for Pioneering Early Detection and Personalized Interventions in Diabetes Management

Diabetes is a long-term metabolic disorder that impacts a significant portion of the global population, frequently resulting in serious health issues, including diabetic foot ul- cers and vision-threatening conditions like diabetic retinopathy. Prompt identification and precise diagnosis of these complications are essential to enable effective treatment and prevent further deterioration. In this study, we propose an automated diagnostic system leveraging deep learning techniques to analyze numeric foot images and retinal scans for diabetic-related abnormalities. By integrating machine learning models with medical imaging, we aim to enhance the efficiency and accuracy of diabetes-related complication detection. Our proposed model utilizes Decision Trees for structured classification and deep learning architectures such as AlexNet and VGG for feature extraction from medical images. The combination of these techniques enables robust and reliable classification of diabetic foot and retinal conditions. The dataset comprises medical images annotated by experts, ensuring high- quality training and evaluation. The hybrid approach effectively captures visual patterns and structured decision-making criteria, leading to improved predictive performance compared to traditional methods. Experimental results demon- strate that our model achieves high classification accuracy in detecting diabetic complications from numeric foot and retinal images. This study advances the field of AI-driven healthcare by developing diagnostic tools that support clinicians in early detection and personalized treatment strategies. Future research directions include enhancing the model's robustness through larger, more diverse datasets and refining its performance for seamless integration into clinical practice.