Diabetic Retinopathy Detection from Retina Image Using Machine Learning

Diabetic Retinopathy is a severe complication of diabetes mellitus and one of the primary causes of blindness on a global scale. Early detection and timely intervention are crucial for preventing irreversible vision loss in affected individuals. This paper focuses on the development of an efficient Diabetic Retinopathy (DR) detection system utilizing machine learning techniques. Leveraging a dataset of high-resolution retina images, the system employs preprocessing methods such as image enhancement and normalization, followed by feature extraction to identify distinctive patterns within the images. Different machine learning algorithms, such as Support Vector Machine (SVM), logistic regression, random forest, and Naive Bayes (NB), undergo training and optimization to categorize the progression of Diabetic Retinopathy (DR) across its mild to severe stages. Additionally, explainable AI techniques are integrated to provide insights into the decision-making process of the models, enhancing transparency and trust in the system's predictions. Moreover, the proposed DR detection system offers a non-invasive, cost-effective, and scalable solution for early diagnosis, facilitating timely medical intervention and preventing the progression of diabetic retinopathy. The paper's integration with Gardio interface enables seamless output for new images, further enhancing its usability. This paper underscores the potential of machine learning in revolutionizing healthcare by contributing to the field of medical image analysis. It aims to improve diagnostic capabilities and, ultimately, enhance patient outcomes.