Journal
IEEE ACCESS
Volume 7, Issue -, Pages 142053-142069Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2019.2944676
Keywords
Feature extraction; Cancer; Lesions; Image analysis; Endoscopes; Gastrointestinal tract; Gastrointestinal disease; gastrointestinal endoscopy image; deep learning; analysis; comparison
Categories
Funding
- National Natural Science Foundation of China [61872405, 61720106004]
- Key Project of Natural Science Foundation of Guangdong Province [2016A030311040]
- Sichuan Science and Technology Support Program [2015SZ0191]
- Fundamental Research Funds for the Central Universities of China [ZYGX2016J189]
- Scientific Platform Improvement Project of UESTC
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Gastrointestinal (GI) disease is one of the most common diseases and primarily examined by GI endoscopy. Recently, deep learning (DL), in particular convolutional neural networks (CNNs) have made achievements in GI endoscopy image analysis. This review focuses on the applications of DL methods in the analysis of GI images. We summarized and compared the latest published literature related to the common clinical GI diseases and covers the key applications of DL in GI image detection, classification, segmentation, recognition, location, and other tasks. At the end, we give a discussion on the challenges and the research directions of GI image analysis based on DL in the future.
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