A novel contrast enhancement technique for diabetic retinal image pre-processing and classification

Background: Diabetic Retinopathy (DR) is a leading cause of blindness among individuals aged 18 to 65 with diabetes, affecting 35–60% of this population, according to the International Diabetes Federation. Early diagnosis is critical for preventing vision loss, yet processing raw fundus images using machine learning faces significant challenges, particularly in accurately identifying microaneurysm lesions, which are crucial for diagnosis. Methods: This study proposes a novel pre-processing technique utilizing the Modified Fuzzy C-means Clustering approach combined with a Support Vector Machine classifier. The method includes converting RGB images to HSI colour space, applying median filtering to reduce noise, enhancing contrast through Intensity Histogram Equalization, and identifying false microaneurysm candidates using connected components. Additionally, morphological operations are performed to remove the optic disc from the enhanced images due to its similarity to microaneurysms. Results: The proposed method was evaluated using publicly available datasets, demonstrating superior performance compared to existing state-of-the-art algorithms. The approach achieved an accuracy rate of 99.31%, significantly improving the detection of microaneurysms and reducing false detections. Conclusions: The findings indicate that the proposed pre-processing technique effectively enhances diabetic retinopathy classification by addressing the challenges of false microaneurysm detection. The comparative analysis against state-of-the-art algorithms highlights the effectiveness of the proposed method, particularly in addressing the challenges associated with false microaneurysms.