Detection and classification of diabetic retinopathy based on ensemble learning

Fundus images are a powerful tool for detecting a variety of retinal disorders. Regular screening of the retina can lead to early detection of conditions like diabetic retinopathy, allowing for timely intervention and treatment. This study is focussed on developing an automated diagnostic system that can accurately detect different stages of diabetic retinopathy. Our approach involves leveraging pre-trained deep learning system to extract important features from fundus images. These features are then employed in a classification system that categorises the images into five stages of retinopathy based on ensemble algorithms. We employ ensemble algorithms like Random forest and XGBoost for classification to improve the accuracy and predictability of the forecast. This drives our focus on enhancing the interpretability and explainability of the model. We trained the model using publicly available fundus images of diabetic individuals for grading and compared the classification results obtained from ensemble techniques with those from deep learning models that used pre-trained weights and biases. The best performing ensemble showed an accuracy range of 0.63 to 0.79. Moreover, the accuracy of 0.96 in detecting the presence of retinopathy provides strong evidence of the approach’s effectiveness, contributing to its reliability, and potential for early diagnosis.