Spotting malignancies from gastric endoscopic images using deep learning

Abstract

Background

Gastric cancer is a common kind of malignancies, with yearly occurrences exceeding one million worldwide in 2017. Typically, ulcerous and cancerous tissues develop abnormal morphologies through courses of progression. Endoscopy is a routinely adopted means for examination of gastrointestinal tract for malignancy. Early and timely detection of malignancy closely correlate with good prognosis. Repeated presentation of similar frames from gastrointestinal tract endoscopy often weakens attention for practitioners to result in true patients missed out to incur higher medical cost and unnecessary morbidity. Highly needed is an automatic means for spotting visual abnormality and prompts for attention for medical staff for more thorough examination.

Methods

We conduct classification of benign ulcer and cancer for gastrointestinal endoscopic color images using deep neural network and transfer-learning approach. Using clinical data gathered from Gil Hospital, we built a dataset comprised of 200 normal, 367 cancer, and 220 ulcer cases, and applied the inception, ResNet, and VGGNet models pretrained on ImageNet. Three classes were defined—normal, benign ulcer, and cancer, and three separate binary classifiers were built—those for normal vs cancer, normal vs ulcer, and cancer vs ulcer for the corresponding classification tasks. For each task, considering inherent randomness entailed in the deep learning process, we performed data partitioning and model building experiments 100 times and averaged the performance values.

Results

Areas under curves of respective receiver operating characteristics were 0.95, 0.97, and 0.85 for the three classifiers. The ResNet showed the highest level of performance. The cases involving normal, i.e., normal vs ulcer and normal vs cancer resulted in accuracies above 90%. The case of ulcer vs cancer classification resulted in a lower accuracy of 77.1%, possibly due to smaller difference in appearance than those cases involving normal.

Conclusions

The overall level of performance of the proposed method was very promising to encourage applications in clinical environments. Automatic classification using deep learning technique as proposed can be used to complement manual inspection efforts for practitioners to minimize dangers of missed out positives resulting from repetitive sequence of endoscopic frames and weakening attentions.

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Acknowledgements

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2018-2-00861, Intelligent SW Technology Development for Medical Data Analysis) and the Gachon University Gil Medical Center (Grant No: 2018-5283). Authors Kim KG and Chung JW equally contributed to this work.

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The authors state that this work has not received any funding.

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Correspondence to Kwang Gi Kim or Jun-Won Chung.

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Authors Jang Hyung Lee, Young Jae Kim, Yoon Woo Kim, Sungjin Park, Yoon Yi Choi, Yoon Jae Kim, Dong Kyun Park, Kwang Gi Kim, and Jun-Won Chung have no financial arrangement or affiliation with any product or services used or discussed in this paper, nor any potential bias against another product or service. The authors declare that there are no conflicts of interest regarding the publication of this paper.

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Lee, J.H., Kim, Y.J., Kim, Y.W. et al. Spotting malignancies from gastric endoscopic images using deep learning. Surg Endosc 33, 3790–3797 (2019). https://doi.org/10.1007/s00464-019-06677-2

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Keywords

  • Gastrointestinal malignancy
  • Endoscopy
  • Ulcer
  • Cancer
  • Deep learning
  • Neural network
  • ResNet