Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle’s APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes.