Abstract
Background: High-quality colonoscopy is essential to prevent the occurrence of colorectal cancers. The data of colonoscopy are mainly stored in the form of images. Therefore, artificial intelligence-assisted colonoscopy based on medical images is not only a research hotspot, but also one of the effective auxiliary means to improve the detection rate of adenomas. This research has become the focus of medical institutions and scientific research departments and has important clinical and scientific research value. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. This method uses the idea of regression, using the entire image as the input of the network and directly returning the target frame of this position in multiple positions of the image. In the feature extraction process, an attention mechanism is added to enhance the contribution of information-rich feature channels and weaken the interference of useless channels; Results: The experimental results show that the method can accurately identify polyp images, especially for the small polyps and the polyps with inconspicuous contrasts, and the detection speed is greatly improved compared with the comparison algorithm. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is also of great significance to the development of clinicians’ clinical work.
Highlights
By 2020, nearly 150,000 people will have been diagnosed with colorectal cancer (CRC), and more than 50,000 people will have died of the disease [2]
It must be mentioned that colorectal cancer has the fastest rising cancer incidence rate in recent years
Resection of polyps can effectively prevent the occurrence of CRC and reduce CRC-related mortality by Studies have shown that colonoscopy is considered to be the gold standard for reducing the incidence rate and mortality of colorectal cancers [5,6]
Summary
In China, the incidence rate of colorectal cancer (CRC) has been increasing year by year. Colorectal cancer is the third and second largest cause of cancer-related death in men and women [1]. This is increasingly affecting people’s health and quality of life. In view of its high incidence rate and mortality, the prevention of CRC is an urgent problem to be solved. Artificial intelligence-assisted colonoscopy based on medical images is a research hotspot, and one of the effective auxiliary means to improve the detection rate of adenomas. Methods: In this paper, we propose a YOLOv5 model based on a self-attention mechanism for polyp target detection. Conclusions: This study will be of great help in reducing the missed diagnosis of clinicians during endoscopy and treatment, and it is of great significance to the development of clinicians’ clinical work
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