With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

中文翻译：

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基于深度迁移学习的内窥镜结肠镜图像多分类

随着人类生活水平的不断提高，饮食习惯也在不断变化，从而带来各种排便问题。其中，结直肠癌的发病率和死亡率一直保持着明显的上升趋势。近年来，深度学习在医学领域的应用越来越广泛和深入。在结肠镜检查中，基于深度学习的人工智能主要用于辅助大肠息肉的检测和大肠病变的分类。但是当涉及到分类时，它可能会导致息肉和其他疾病之间的混淆。为了准确诊断肠道各种疾病，提高息肉分类准确率，本文提出了一种基于深度学习的医学结肠镜图像多分类方法，主要分为息肉、炎症、肿瘤、正常四种情况。鉴于数据集相对较少，首先在ImageNet上通过迁移学习训练的网络作为预训练模型，将从源域学习任务中学到的先验知识应用于肠道疾病分类任务。然后，我们对模型进行微调，使其更适合我们的数据集进行肠道分类的任务。最后，将该模型应用于医学结肠镜图像的多分类。实验结果表明，本工作中的方法在保证其他类别分类准确率的同时，能够显着提高息肉的识别率，从而辅助医生进行手术切除的诊断。