A Convolutional Neural Network Model Using Weighted Loss Function to Detect Diabetic Retinopathy

Nowadays, artificial intelligence (AI) provides tremendous prospects for driving future healthcare while empowering patients and service providers. The extensive use of digital healthcare produces a massive amount of multimedia healthcare data continuously (e.g., MRI, X-Ray, ultrasound images, etc.). Hence, it needs special data analytics techniques to provide a smart diagnosis to the patients. Recent advancements in artificial intelligence and machine learning techniques, particularly Deep learning (DL) methods, have demonstrated tremendous medical diagnosis progress and achievements. Diabetic Retinopathy (DR), cataract, macular degeneration, and glaucoma are the most conunon eye problems due to diabetes. Numerous models have been proposed using deep learning models to diagnose diabetic retinopathy, but no model is perfect for detecting DR diseases. This article presents a deep learning model to analyze diabetic retinopathy images to classify DR patients' severity levels. The model applies a custom-weighted loss function in the model's training and achieves 92.49% accuracy and a 0.945 Cohen Kappa score on test data. The model's weighted average precision was 93%, recall 92%, and f1 score 93%. The model is compared with several state-of-the-art pre-trained models. We observe that the proposed model performs better in accuracy results and Cohen Kappa score.