Efficient polyp detection algorithm based on deep learning

Abstract

Objective: Colon polyp detection is crucial in reducing the incidence of colorectal cancer. However, due to the diverse morphology of colon polyps, their high similarity to surrounding tissues, and the difficulty of detecting small target polyps, false negatives and false positives are common problems.

Methods: To address this, we propose a lightweight and efficient colon polyp detection model based on YOLOv10, a deep learning-based object detection method-EP-YOLO (Efficient for Polyp). By introducing the GBottleneck module, we reduce the number of parameters and accelerate inference; a lightweight GHead detection head and an additional small target detection layer are designed to enhance small target recognition ability; we propose the SE_SPPF module to improve attention on polyps while suppressing background noise interference; the loss function is replaced with Wise-IoU to optimize gradient distribution and improve generalization ability.

Results: Experimental results on the publicly available LDPolypVideo (7,681 images), Kvasir-SEG (1,000 images) and CVC-ClinicDB (612 images) datasets show that EP-YOLO achieves precision scores of 94.17%, 94.32% and 93.21%, respectively, representing improvements of 2.10%, 2.05% and 1.42% over the baseline algorithm, while reducing the number of parameters by 16%.

Conclusion: Compared with other mainstream object detection methods, EP-YOLO demonstrates significant advantages in accuracy, computational load and FPS, making it more suitable for practical medical scenarios in colon polyp detection.

Keywords: Colon polyp detection; artificial intelligence; colorectal cancer; deep learning; medical images.