Department of General Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China.
Department of Pharmaceutics, College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110116, China.
Comput Math Methods Med. 2021 Jul 3;2021:2485934. doi: 10.1155/2021/2485934. eCollection 2021.
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.
随着人类生活水平的不断提高，饮食习惯不断变化，带来了各种肠道问题。其中，结直肠癌的发病率和死亡率一直呈显著上升趋势。近年来，深度学习在医学领域的应用在国外得到了越来越广泛和深入的发展。在结肠镜检查中，基于深度学习的人工智能主要用于辅助检测结直肠息肉和结直肠病变的分类。但在分类方面，它可能会导致息肉和其他疾病之间的混淆。为了准确诊断肠道中的各种疾病并提高息肉的分类准确性，本工作提出了一种基于深度学习的医学结肠镜图像多分类方法，主要对息肉、炎症、肿瘤和正常四种情况进行分类。鉴于数据集相对较少，首先使用在 ImageNet 上进行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关于肠道疾病的分类任务。然后，我们使用我们的数据集来微调模型，使其更适合肠道分类任务。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，本工作中的方法可以在保证其他类别分类准确性的同时，显著提高息肉的识别率，从而辅助医生进行手术切除诊断。
