基于深度学习的结肠息肉检测算法

Colon polyp detection algorithm based on deep learning

近年来,受结肠疾病困扰的人群数量不断增加,由于诊断不及时,结肠疾病容易演变成结肠癌,严重威胁患者的生命健康。结肠息肉是结肠癌在患者体内的前期表征,通过结肠镜及时准确地检测结肠息肉并进行干预可以降低结肠癌发生的概率。目前,深度学习在医学图像处理领域应用广泛,在结肠镜图像息肉检测中应用深度学习技术,可以帮助医生进行精确诊断。针对传统方法在肠息肉检测中因息肉成像差异大、肠息肉类型多样等存在假阳性高的问题,利用U-net++网络对结肠息肉进行检测,提出一种引入注意力机制的U-net++改进模型,采用端到端的结构,并针对不同网络进行实验研究,对不同网络模型在结肠息肉检测结果进行精度和损失度对比分析。经过实验验证,引入注意力机制的U-net++改进网络模型能够快速精确地检测结肠息肉,可以更好地辅助医生进行临床决策和干预,具有重要的研究意义和临床应用价值。

In recent years,there has been an increase in the number of people affected by colon disease.Because the diagnosis is not timely,the colon disease is easy to evolve into colon cancer,and the life and health of patients will be seriously threatened.Colon polyp is the early manifestation of colon cancer in patients,and colon endoscopy is the most effective means to detect colon polyp.At present,deep learning stands out in the field of medical image processing.The application of deep learning technology in colonoscopy polyp detection can help doctors make accurate diagnosis.In view of the problems of high false positives in intestinal polyp detection by traditional methods due to large imaging differences and diverse types of intestinal polyps,U-net++network has been used to detect colon polyps,and an improved U-net++model introducing attention mechanism was proposed,which adopted end-to-end structure and conducted experimental studies on different networks.The precision and loss degree of colon polyp detection have been compared with different network models.Through experimental verification,the improved U-net++network model introduced by attention mechanism can quickly and accurately detect colon polyps,which can better assist doctors in clinical decision-making and intervention,and has important research significance and clinical application value.