Cheetah optimized CNN: A bio-inspired neural network for automated diabetic retinopathy detection

The escalating global prevalence of diabetes has underscored the critical need for effective screening and diagnosis of diabetic retinopathy (DR), a common complication of diabetes that can lead to irreversible vision loss. In this study, we propose a novel algorithm for automated DR detection in retinal fundus images using deep learning techniques. The algorithm incorporates a cheetah optimized convolutional neural network (CO-CNN) that draws inspiration from cheetah hunting behavior for efficient image processing, segmentation, feature extraction, and classification. Preprocessing steps involve median filter and contrast limited adaptive histogram equalization to enhance image quality. The segmented output is clustered using the cascaded fuzzy C-means algorithm and features are extracted with the speeded-up robust features algorithm. The experimental results on the Indian Diabetic Retinopathy Image Dataset demonstrate an accuracy of 98.64% in predicting various stages of DR. The proposed CO-CNN approach shows superior performance compared to that of state-of-the-art methods, offering potential applications in telemedicine, treatment planning, early detection, screening, and patient education. Integrating fuzzy logic enhances the model’s interpretability and robustness, paving the way for improved healthcare outcomes in diabetic retinopathy management.