基于深度学习的慢性萎缩性胃炎诊断

慢性萎缩性胃炎是一种常见的胃病，如果得不到及时治疗，有可能发展成胃癌。然而，胃镜检查在萎缩性胃炎检查 中的敏感性仅为约42％，且活检受许多因素的影响。因此，使用卷积神经网络有助于提高诊断慢性萎缩性胃炎的准确性。 首先采用INPAINT_TELEA 算法对胃窦图像进行预处理，去除图像中的水印，对残差网络进行改进并嵌入 Squeeze_and_Excitaion模块以筛查慢性萎缩性胃炎，改进后的网络（SR-CAGnet）通过建立短路机制以及采用特征重标定 策略提高图像的分类效果。结果表明:与Alexnet和改进的ResNet网络进行对比，SR-CAGnet对慢性萎缩性胃炎的检出 率为87.92％，算法识别效果良好。通过使用Apriori算法并分析，得到萎缩性胃炎与胃镜检查下其他症状的关系，以辅助 医生的诊断。最后使用CAM热图验证模型的有效性。

Diagnosis of chronic atrophic gastritis based on deep learning

Chronic atrophic gastritis is a common stomach disease, and it may develop into gastric cancer if without timely treatment. However, the sensitivity of gastroscopy in the examination of atrophic gastritis is only about 42%, and the biopsy is affected by many factors. Therefore, convolutional neural network is used to improve the diagnosis accuracy of chronic atrophic gastritis. INPAINT_TELEA algorithm is firstly used to preprocess the image of the gastric antrum for removing the watermarks in the image, and then residual network is improved and embed into Squeeze_and_Excitation module to realize the diagnosis of chronic atrophic gastritis. Finally, the improved network (SR-CAGnet) is applied to enhance the classification effect of images by establishing a short circuit mechanism and adopting a feature recalibration strategy. The results show that the detection rate of chronic atrophic gastritis by SR-CAGnet reaches 87.92% as compared with Alexnet and improved ResNet, which indicates the proposed algorithm has a good performance on recognition. Through the analysis by Apriori algorithm, the relationship between atrophic gastritis and other symptoms detected by gastroscopy is obtained, thus assisting doctors diagnosis. Finally, the validity of the model is verified using CAM heat map.