Early Detection and Classification of Diabetic Retinopathy: A Deep Learning Approach

Background—Diabetes is a rapidly spreading chronic disease that poses a significant risk to individual health as the population grows. This increase is largely attributed to busy lifestyles, unhealthy eating habits, and a lack of awareness about the disease. Diabetes impacts the human body in various ways, one of the most serious being diabetic retinopathy (DR), which can result in severely reduced vision or even blindness if left untreated. Therefore, an effective early detection and diagnosis system is essential. As part of the Kingdom of Saudi Arabia’s Vision 2030 initiative, which emphasizes the importance of digital transformation in the healthcare sector, it is vital to equip healthcare professionals with effective tools for diagnosing DR. This not only ensures high-quality patient care but also results in cost savings and contributes to the kingdom’s economic growth, as the traditional process of diagnosing diabetic retinopathy can be both time-consuming and expensive. Methods—Artificial intelligence (AI), particularly deep learning, has played an important role in various areas of human life, especially in healthcare. This study leverages AI technology, specifically deep learning, to achieve two primary objectives: binary classification to determine whether a patient has DR, and multi-class classification to identify the stage of DR accurately and in a timely manner. The proposed model utilizes six pre-trained convolutional neural networks (CNNs): EfficientNetB3, EfficientNetV2B1, RegNetX008, RegNetX080, RegNetY006, and RegNetY008. In our study, we conducted two experiments. In the first experiment, we trained and evaluated different models using fundus images from the publicly available APTOS dataset. Results—The RegNetX080 model achieved 98.6% accuracy in binary classification, while the EfficientNetB3 model achieved 85.1% accuracy in multi-classification, respectively. For the second experiment, we trained the models using the APTOS dataset and evaluated them using fundus images from Al-Saif Medical Center in Saudi Arabia. In this experiment, EfficientNetB3 achieved 98.2% accuracy in binary classification and EfficientNetV2B1 achieved 84.4% accuracy in multi-classification, respectively. Conclusions—These results indicate the potential of AI technology for early and accurate detection and classification of DR. The study is a potential contribution towards improved healthcare and clinical decision support for an early detection of DR in Saudi Arabia.