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Hardware-Efficient Pruned CNN Optimized by Neural Architecture Search and Genetic Algorithm for Diabetic Retinopathy Detection on STM32F746

Authors :

Omid Askari Haddad¹ Sara Ershadi-Nasab²

1- MSc Student 2- Assistant Professor

Keywords :

Diabetic retinopathy detection،Genetic algorithm،Convolutional neural network،NSGA-II algorithm،Arm microcontroller

Abstract :

Scalable screening for diabetic retinopathy remains difficult to deliver where it is most needed. We present a hardware-efficient convolutional neural network (CNN) designed for reliable, on-device DR detection. Our approach integrates a compact CNN architecture with structured pruning and a Non-dominated Sorting Genetic Algorithm II (NSGA-II) to work under strict memory and compute budgets. We validate the approach on standard DR datasets and demonstrate deployment on an ARM microcontroller, highlighting its practical feasibility for portable screening tools in rural and underserved clinics. This work contributes (1) an end-to-end, resource-aware pipeline that couples architecture search with pruning, (2) a principled optimization strategy that balances diagnostic accuracy and efficiency, and (3) an embedded deployment that illustrates scalable, accessible AI-driven DR screening.

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