Gastrointestinal mucosal changes can cause cancers after some years and early diagnosing them can be very useful to prevent cancers and early treatment. In this article, 8 classes of mucosal changes and anatomical landmarks including Polyps, Ulcerative Colitis, Esophagitis, Normal Z-Line, Normal Pylorus, Normal Cecum, Dyed Lifted Polyps, and Dyed Lifted Margin were predicted by deep learning. We used neural networks in this article. It is a black box artificial intelligence algorithm that works like a human neural system. In this article, Transfer Learning (TL) based on the Convolutional Neural Networks (CNNs), which is one of the well-known types of neural networks in image processing is used. We compared some famous CNN architecture including VGG, Inception, Xception, and ResNet. Our best model got 93% accuracy in test images. At last, we used our model in some real endoscopy and colonoscopy movies to classify problems.

胃肠道粘膜变化可导致癌症，在此研究中利用深度学习技术预测了 polyps（息肉）、ulcerative colitis（溃疡性结肠炎）、esophagitis（食管炎）、normal z-line（正常z线）、normal pylorus（正常幽门）、normal cecum（正常盲肠）、dyed lifted polyps（涂染抬起息肉）和dyed lifted margin（涂染抬起边缘）等八类粘膜变化和解剖标志，作者采用卷积神经网络（CNN）结构的迁移学习（TL）模型，并通过与VGG、Inception、Xception和ResNet等不同CNN架构进行比较，最佳模型在测试图像中达到了93%的准确率，最后，作者将模型应用于一些实际内窥镜和结肠镜的影像数据进行问题分类。

深度学习对胃肠黏膜问题进行分类