School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China.
Department of Gastroenterology, Jiading Central Hospital Affiliated to Shanghai University of Medicine and Health Sciences, Shanghai, 201899, China.
Sci Rep. 2022 Sep 13;12(1):15365. doi: 10.1038/s41598-022-19639-x.
To explore the application value of convolutional neural network combined with residual attention mechanism and Xception model for automatic classification of benign and malignant gastric ulcer lesions in common digestive endoscopy images under the condition of insufficient data. For the problems of uneven illumination and low resolution of endoscopic images, the original image is preprocessed by Sobel operator, etc. The algorithm model is implemented by Pytorch, and the preprocessed image is used as input data. The model is based on convolutional neural network for automatic classification and diagnosis of benign and malignant gastric ulcer lesions in small number of digestive endoscopy images. The accuracy, F1 score, sensitivity, specificity and precision of the Xception model improved by the residual attention module for the diagnosis of benign and malignant gastric ulcer lesions were 81.411%, 81.815%, 83.751%, 76.827% and 80.111%, respectively. The superposition of residual attention modules can effectively improve the feature learning ability of the model. The pretreatment of digestive endoscopy can remove the interference information on the digestive endoscopic image data extracted from the database, which is beneficial to the training of the model. The residual attention mechanism can effectively improve the classification effect of Xception convolutional neural network on benign and malignant lesions of gastric ulcer on common digestive endoscopic images.
探讨卷积神经网络结合残差注意力机制和 Xception 模型在数据不足的情况下对普通消化内镜图像中良性和恶性胃溃疡病变进行自动分类的应用价值。针对内镜图像光照不均匀、分辨率低等问题，采用 Sobel 算子等对原始图像进行预处理。算法模型基于 Pytorch 实现，将预处理后的图像作为输入数据。该模型基于卷积神经网络，对少量消化内镜图像中的良性和恶性胃溃疡病变进行自动分类和诊断。残差注意力模块改进后的 Xception 模型对良性和恶性胃溃疡病变的诊断准确性、F1 评分、灵敏度、特异性和精度分别为 81.411%、81.815%、83.751%、76.827%和 80.111%。残差注意力模块的叠加可以有效提高模型的特征学习能力。消化内镜的预处理可以去除从数据库中提取的消化内镜图像数据上的干扰信息，有利于模型的训练。残差注意力机制可以有效提高 Xception 卷积神经网络对普通消化内镜图像中胃溃疡良恶性病变的分类效果。
