Research Information System
2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA), Ankara, Turkey, 23 May 2025, pp.1-8, (Full Text)
This paper explores the optimization of convolutional neural networks (CNNs) for ophthalmic disease detection via fundus image classification on resource-constrained edge devices. The study investigates the impact of pruning, quantization, and knowledge distillation on reducing model size and inference time while maintaining high classification accuracy. Using a dataset of retinal fundus images, the optimized models were deployed on a Jetson Nano microcontroller. Experimental results demonstrate that post-training quantization achieves the best trade-off between efficiency and accuracy, reducing model size to 11.22MB while maintaining a validation accuracy of 97.39%. Hybrid approaches combining knowledge distillation and quantization further reduced parameters while preserving performance, highlighting promising strategies for deploying deep learning in edge AI healthcare applications.