ArticleComputational and mathematical methods in medicine2022
White-Light Endoscopic Colorectal Lesion Detection Based on Improved YOLOv5.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 12 citations in OpenAlex.
- Gastrointestinal polyp detection method based on the improved RT-DETR.Scientific reports · 2026Article
- A colonic polyps detection algorithm based on an improved YOLOv5s.Scientific reports · 2025Article
- Detection of Pine Wilt Nematode from Drone Images Using UAV.Sensors (Basel, Switzerland) · 2022Article
- Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.BioMed research international · 2022Article
- Efficient Synchronous Real-Time CADe for Multicategory Lesions in Gastroscopy by Using Multiclass Detection Model.BioMed research international · 2022Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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Abstract
As an effective tool for colorectal lesion detection, it is still difficult to avoid the phenomenon of missed and false detection when using white-light endoscopy. In order to improve the lesion detection rate of colorectal cancer patients, this paper proposes a real-time lesion diagnosis model (YOLOv5x-CG) based on YOLOv5 improvement. In this diagnostic model, colorectal lesions were subdivided into three categories: micropolyps, adenomas, and cancer. In the course of convolutional network training, Mosaic data enhancement strategy was used to improve the detection rate of small target polyps. At the same time, coordinate attention (CA) mechanism was introduced to take into account channel and location information in the network, so as to realize the effective extraction of three kinds of pathological features. The Ghost module was also used to generate more feature maps through linear processing, which reduces the stress of learning model parameters and speeds up detection. The experimental results show that the lesion diagnosis model proposed in this paper has a more rapid and accurate lesion detection ability, and the AP value of polyps, adenomas, and cancer is 0.923, 0.955, and 0.87, and mAP@50 is 0.916.
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