Diagnosis of chronic atrophic gastritis based on deep learning(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第5期
- Page:
- 649-655
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Diagnosis of chronic atrophic gastritis based on deep learning
- 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
- Keywords:
- chronic atrophic gastritis deep learning Squeeze_and_ExcitaionApriori algorithm
- PACS:
- R318;R573.32
- DOI:
- 10.3969/j.issn.1005-202X.2020.05.023
- 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.
Last Update: 2020-06-03