Enhanced diagnosis of diabetic retinopathy: integrating advanced algorithms for automated detection and classification

To develop an automated diagnostic system for early detection of diabetic retinopathy (DR) using fundus images by identifying exudates, hemorrhages, and microaneurysms with advanced image processing and machine learning techniques.

Fundus images from the IDRiD dataset and additional Kaggle datasets were used. A wavelet-based band-pass filter was applied for edge enhancement of retinal features. Gaussian mixture model (GMM) clustering was used to segment and extract texture features. These extracted features were classified using machine learning algorithms, including a random forest classifier and a multilayer perceptron neural network. Performance metrics such as sensitivity, specificity, and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.

The random forest-based classification system achieved a sensitivity of 95.08%, specificity of 86.67%, and overall accuracy of 95.20% in detecting DR lesions. The combination of wavelet-based edge enhancement, GMM clustering, and neural network-based feature classification demonstrated high reliability in lesion identification.

The proposed method effectively detects early signs of DR from fundus images, offering a high-accuracy, automated, and scalable solution for assisting ophthalmologists. Its application can support large-scale screening programs, particularly in regions with limited access to specialized eye care.