[1]巩稼民,马豆豆,蒋杰伟,等.基于深度学习的慢性萎缩性胃炎诊断[J].中国医学物理学杂志,2020,37(5):649-655.[doi:10.3969/j.issn.1005-202X.2020.05.023]
　GONG Jiamin,MA Doudou,JIANG Jiewei,et al.Diagnosis of chronic atrophic gastritis based on deep learning[J].Chinese Journal of Medical Physics,2020,37(5):649-655.[doi:10.3969/j.issn.1005-202X.2020.05.023]
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基于深度学习的慢性萎缩性胃炎诊断()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第5期
页码:
649-655
栏目:
医学影像物理
出版日期:
2020-05-25

文章信息/Info

Title:
Diagnosis of chronic atrophic gastritis based on deep learning
文章编号:
1005-202X（2020）05-0649-07
作者:
巩稼民1; 马豆豆1; 蒋杰伟1; 张雅琼2; 裴梦杰3
1. 西安邮电大学电子工程学院，陕西西安710121；2. 山西医科大学附属山西省人民医院消化科，山西太原030012；3. 西安邮电 大学计算机学院，陕西西安710121
Author(s):
GONG Jiamin1;  MA Doudou1;  JIANG Jiewei1;  ZHANG Yaqiong2;  PEI Mengjie3
1. School of Electronic Engineering, Xian University of Posts and Telecommunications, Xian 710121, China 2. Department of Gastroenterology, Shanxi Provincial Peoples Hospital of Shanxi Medical University, Taiyuan 030012, China 3. School of Computer Science & Technology, Xian University of Posts and Telecommunications, Xian 710121, China
关键词:
慢性萎缩性胃炎; 深度学习; Squeeze_and_Excitaion; Apriori算法
Keywords:
chronic atrophic gastritis deep learning Squeeze_and_ExcitaionApriori algorithm
分类号:
R318；R573.32
DOI:
10.3969/j.issn.1005-202X.2020.05.023
文献标志码:
A
摘要:
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。
Abstract:
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.

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备注/Memo

备注/Memo:
【收稿日期】2019-12-19 【基金项目】国家重点研发计划（2018YFC0116500）；中央高校基本科 研业务费专项资金资助项目（JB181002） 【作者简介】巩稼民，教授，主要从事光通信研究、图像处理，E-mail: gjm@xupt.edu.cn；马豆豆，研究生，主要从事深度学习医疗 影像处理，E-mail: mdd615912588@163.com 【通信作者】蒋杰伟，博士，主要从事深度学习医疗影像处理，E-mail: jiangjw924@126.com
更新日期/Last Update: 2020-06-03