This study aims to develop a deep learning object detection model capable of autonomously locating polyps and classifying them according to the NICE classification, thereby facilitating more effective diagnostic and treatment planning.

This study utilized four colorectal polyp datasets collected from Changshu Hospital Affiliated to Soochow University, Changshu Traditional Chinese Medicine Hospital and Changshu Xinzhuang People's Hospital from January 2018 to June 2023, comprising both static images and videos. All samples were pathologically confirmed and classified according to the NICE typology. The images were annotated using the LabelMe tool and subsequently converted to MSCOCO format to suit deep learning model training. We employed a pre-trained Faster R-CNN model, combined with real-time data augmentation and various image processing techniques, and trained the model using a transfer learning strategy. Model performance was evaluated following the COCO standards, focusing on Intersection over Union (IoU), Average Precision (AP), and recall rate. Additionally, the model's capability in NICE classification recognition was thoroughly assessed.

We analyzed 2 248 colorectal polyps from 1 835 patients, all pathologically confirmed and classified as 575 NICE type 1, 1 143 NICE type 2, and 530 NICE type 3. Using transfer learning techniques, Faster R-CNN models based on ResNet-50 and ResNet-101 backbone networks were developed, with the ResNet-101-based version named Faster R-CNN-NICE. Performance analysis showed that although the Faster R-CNN-NICE model experienced a slight decrease in processing speed (reduction by 1.72 frames per second), it significantly improved in bounding box average precision (bbox_mAP reaching 0.542) and comprehensive average precision for polyp classification (mAP at 0.830). Compared to endoscopists, this model demonstrated higher confidence and accuracy in most cases, particularly in predicting NICE type 2 polyps, where its performance significantly differed from that of the doctors (χ²=4.30, P<0.05). After conversion to the ONNX format, the model exhibited optimized execution efficiency in various hardware environments and effectively achieved rapid localization and accurate classification of polyps in real-time video detection.

This research constructed a colon polyp image dataset based on the NICE classification system and developed a Faster R-CNN-NICE deep learning model, which effectively achieves immediate localization and precise identification of colon polyps. This technology is expected to support the clinical work of endoscopists, significantly enhancing the efficiency and accuracy of diagnoses.