Newton’s law-inspired modified PSO for hyperparameter tuning of deep transfer learning models in multiclass diabetic retinopathy classification

Diabetic Retinopathy (DR) is a major cause of vision impairment worldwide and automated analysis of retinal fundus images can support timely disease screening and severity assessment. This paper presents an end-to-end deep learning framework for five-class DR classification using the Indian Diabetic Retinopathy Image Database (IDRiD). The framework integrates image preprocessing, controlled data augmentation, an Approximate Class Balancer (ACB), transfer learning and Newtonian Particle Swarm Optimization (NPSO)-based hyperparameter tuning. Three pretrained architectures, namely VGG16, VGG19 and MobileNet are systematically evaluated under progressive experimental settings. NPSO incorporates Newtonian force dynamics, particle mass and elapsed-iteration-dependent attraction to improve search flexibility during learning-rate and dropout optimization. The best performance is obtained using NPSO-optimized MobileNet achieving an accuracy of 99.60%. Five independent runs further yield a mean accuracy of 99.13 ± 0.18% , demonstrating consistent performance across stochastic initializations. Comparative hyperparameter optimization experiments show that NPSO provides the highest overall mean accuracy with comparatively low configuration-wise variability. The results demonstrate a lightweight NPSO-optimized transfer learning for computationally efficient multiclass DR screening.