Automated Grading of Diabetic Retinopathy Severity Using Convolutional Neural Networks and Particle Swarm Optimization-Based Hyperparameter Tuning
Keywords:
Optimization, CNN, Diabetic Retinopathy, PSOAbstract
A frequent side effect of diabetes and the main global cause of blindness is diabetic retinopathy (DR). For effective management and therapy, DR severity must be diagnosed quickly and accurately. In this paper, we propose an automated DR severity assessment system utilising convolutional neural networks (CNNs) and optimise its performance with hyperparameter tweaking based on particle swarm optimisation (PSO).A large dataset of retinal pictures with DR severity grades are the foundation of our method. We use a cutting-edge CNN architecture, which has proven to have outstanding performance in picture classification applications. In order to classify retinal images into several DR severity categories, ranging from mild to proliferative, the CNN is trained to extract pertinent information from retinal images.We utilise PSO-based hyperparameter adjustment to improve the performance of the CNN. A metaheuristic optimisation algorithm called PSO efficiently looks for ideal hyperparameters like learning rates, dropout rates, and batch sizes. We improve the CNN's capacity to generalise and generate precise predictions on unobserved data by optimising these parameters.A large and varied dataset of retinal pictures is used to evaluate the proposed method, which aims to achieve high sensitivity and specificity in evaluating DR severity. Our findings show that the system can reliably categorise DR severity levels, even in the presence of modest and complex retinal anomalies.Automation of DR severity grading through the use of CNNs and PSO-based hyperparameter tweaking offers a promising option, delivering rapid and accurate assessments. This method may help medical personnel identify and monitor DR earlier, hence improving patient outcomes and lightening the load on healthcare systems. The use of this system in clinical situations and further performance optimisation may be the subject of future development.
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