Helicobacter Pylori infection detection from gastric X-ray images based on feature fusion and decision fusion

Ishihara, Kazuhíko; Ogawa, Takahiro; Haseyama, Miki

doi:10.1016/j.compbiomed.2017.03.007

Cited by 8 publications

(3 citation statements)

References 46 publications

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“…Considering the application of deep learning techniques for automated gastritis detection, further investigations using data for various facilities will enhance the reliability of deep learning techniques. Our previous studies using 16,800 X-ray images from other medical facilities has already shown the effectiveness of machine learning techniques [27][28][29], it can be considered that deep learning techniques for UGI-XR have bright prospects.…”

Section: Discussionmentioning

confidence: 99%

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

et al. 2018

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Background: Deep learning has become a new trend of image recognition tasks in the field of medicine. We developed an automated gastritis detection system using double-contrast upper gastrointestinal barium X-ray radiography. Methods: A total of 6,520 gastric X-ray images obtained from 815 subjects were analyzed. We designed a deep convolutional neural network (DCNN)-based gastritis detection scheme and evaluated the effectiveness of our method. The detection performance of our method was compared with that of ABC (D) stratification. Results: Sensitivity, specificity, and harmonic mean of sensitivity and specificity of our method were 0.962, 0.983, and 0.972, respectively, and those of ABC (D) stratification were 0.925, 0.998, and 0.960, respectively. Although there were 18 false negative cases in ABC (D) stratification, 14 of those 18 cases were correctly classified into the positive group by our method. Conclusions: Deep learning techniques may be effective for evaluation of gastritis/non-gastritis. Collaborative use of DCNN-based gastritis detection systems and ABC (D) stratification will provide more reliable gastric cancer risk information.

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Section: Discussionmentioning

confidence: 99%

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

et al. 2018

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“…Ishihara et al. used MKL to build a system that can automatically detect H. pylori infection [ 112 ]. The MKL algorithm essentially defines M base kernel functions and uses a weighted linear combination of the base functions as the kernel function of the SVM.…”

Section: Ai Systems For Prediction Of H Pylori Inf...mentioning

confidence: 99%

Artificial intelligence in theranostics of gastric cancer, a review

et al. 2023

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Gastric cancer (GC) is one of the commonest cancers with high morbidity and mortality in the world. How to realize precise diagnosis and therapy of GC owns great clinical requirement. In recent years, artificial intelligence (AI) has been actively explored to apply to early diagnosis and treatment and prognosis of gastric carcinoma. Herein, we review recent advance of AI in early screening, diagnosis, therapy and prognosis of stomach carcinoma. Especially AI combined with breath screening early GC system improved 97.4 % of early GC diagnosis ratio, AI model on stomach cancer diagnosis system of saliva biomarkers obtained an overall accuracy of 97.18 %, specificity of 97.44 %, and sensitivity of 96.88 %. We also discuss concept, issues, approaches and challenges of AI applied in stomach cancer. This review provides a comprehensive view and roadmap for readers working in this field, with the aim of pushing application of AI in theranostics of stomach cancer to increase the early discovery ratio and curative ratio of GC patients.

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“…For realizing CAD systems, researchers have been exploring methods for CAG detection from GXIs[ 14 - 17 ]. In early works, attempts were made to describe the visual features of CAG with mathematical models[ 14 , 15 ]. For more accurate detection, in the papers[ 16 , 17 ], we have tried to introduce convolutional neural networks (CNNs)[ 18 ] since it has been reported that CNNs outperform methods with hand-crafted features in various tasks[ 19 - 21 ].…”

Section: Introductionmentioning

confidence: 99%

Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions

Kanai

Togo

Ogawa

et al. 2020

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BACKGROUND The risk of gastric cancer increases in patients with Helicobacter pylori -associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections. AIM To evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection. METHODS We used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training. RESULTS In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively. CONCLUSION By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.

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Helicobacter Pylori infection detection from gastric X-ray images based on feature fusion and decision fusion

Cited by 8 publications

References 46 publications

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography

Artificial intelligence in theranostics of gastric cancer, a review

Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions

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