Smart Diagnosis System For Diabetic Retinopathy

Diabetic retinopathy (DR) presents a substantial vision health challenge, demanding early detection. However, limited ophthalmologist resources and meticulous manual diagnosis hinder swift and precise identification. Leveraging cutting-edge advances in smart healthcare and AI, we introduce an innovative solution-a groundbreaking DR diagnostic system powered by EfficientNet CNN and transfer learning. Utilizing the APTOS 2019 Blindness Detection dataset ensures both efficiency and accuracy. Notably, our system stands out by offering a simultaneous diagnosis of various retinal diseases, setting it apart from previous efforts. Impressively, our model achieves a remarkable 93.80% accuracy, surpassing benchmarks across dimensions. Guided by user-friendly interfaces, doctors and assistants seamlessly input data, generate medical cards, and access patient reports. Balancing design, ImageNet pre-training, and deep learning, our solution bridges ophthalmologist gaps, providing precise DR detection. Its potential extends further, promising improved patient care and broader medical applications. Amid AI-driven healthcare transformation, our pioneering intelligent diagnostic system shines as a beacon of positive impact.