EfficientNet-based Deep Learning Framework for Early Detection of Diabetic Retinopathy in Retinal Fundus Images

The rapid advancement of machine learning and computer vision has opened new pathways for early disease detection across various fields. This paper presents a new approach in computer vision involving a machine learning algorithm for the early diagnosis of Diabetic Retinopathy (DR), one of the major causes of blindness in working-age adults. Diagnosis should be as early as possible to ensure timely intervention before the onset of more serious disease processes and thus preserve vision. The existing approach to diagnosis is time cycle based, using subjective, manual analysis, and guided by the health infrastructure available, significantly limited in geographically challenged areas. Our approach is making use of deep learning over retinal fundus images, our model does provide an efficient, automated diagnostic tool for DR with the hopes of improving the diagnostic accuracy, reducing the time taken in the assessment, and helping healthcare providers make decisions promptly. The integration of Convolution Neural Network technology with EfficientNet method holds promise in the advancement of practices related to eye care and the minimization of negative impact on healthcare systems around the world due to DR.