Optimizing Convolutional Neural Networks with Nature-Inspired Algorithms for Diabetic Retinopathy Classification
Abstract
Diabetic Retinopathy (DR) is a progressive eye disorder caused by prolonged diabetes and is recognized as one of the leading causes of vision impairment and blindness worldwide. The disease affects the small blood vessels of the retina, resulting in structural damage that gradually deteriorates visual capability. Early detection and continuous monitoring of DR are essential to prevent severe complications and permanent vision loss. However, manual examination of retinal fundus images by ophthalmologists is time-consuming and requires significant expertise, especially when dealing with large-scale screening programs. Consequently, automated diagnostic systems based on artificial intelligence have gained considerable attention in recent years. Advancements in medical imaging and machine learning have enabled the development of intelligent models capable of detecting retinal abnormalities with high precision. The classification of DR severity levels from digital fundus images. To further enhance the performance of the CNN model, Nature-Inspired Algorithms (NIAs) are incorporated as optimization techniques. These algorithms mimic natural evolutionary and behavioral processes to improve model parameters and learning efficiency. Several NIAs are investigated in order to identify the most effective optimization strategy for improving classification performance.
Among the evaluated approaches, Particle Swarm Optimization (PSO) demonstrated superior capability in optimizing the CNN architecture by effectively adjusting network parameters and improving feature learning. The proposed hybrid CNN–PSO model achieved an overall classification accuracy of 98.83%, outperforming several existing state-of-the-art methods reported in the literature. The results highlight the effectiveness of integrating nature-inspired optimization strategies with deep learning frameworks for medical image analysis. This approach offers a reliable and efficient solution for automated DR screening and can significantly support ophthalmologists in early diagnosis and clinical decision-making.
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