Automated Polyp Segmentation Using Evolutionary Neural Architecture Search

Colorectal cancer is a major cause of cancer mortality, with a significant proportion of polyps being missed during colonoscopy. To address this challenge, we propose EvoPolypNet, an automated polyp segmentation framework optimized through evolutionary neural architecture search (NAS). EvoPolypNet integrates convolutional feature extraction, attention mechanisms, multi-scale processing, and feature recalibration within an encoder-decoder architecture. The evolutionary NAS employs crossover, mutation, and elitism to explore diverse architectural configurations, guided by a defined search space. Evaluated on CVC-ClinicDB and Kvasir-SEG datasets, EvoPolypNet achieves superior performance with an mDice of 0.939 and mIoU of 0.898 on CVC-ClinicDB and demonstrates strong generalization on the unseen Kvasir-SEG dataset with an mDice of 0.919. The resulting model contains only 2.3 million parameters, enabling high segmentation accuracy with computational efficiency suitable for clinical deployment. Visual comparisons further validate the model's precision in delineating polyp boundaries. EvoPolyp-Net establishes a new benchmark for polyp segmentation by balancing performance, generalization, and efficiency.