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Automatic detection of colorectal polyps using artificial intelligence techniques
Vol 2, Issue 1, 2021
Issue release: 30 June, 2021
VIEWS - 2549 (Abstract)
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Abstract
Colorectal cancer (CRC) is one of the most prevalent malignant tumors in Colombia and the world. These neoplasms originate in adenomatous lesions or polyps that must be resected to prevent the disease, which can be done with a colonoscopy. It has been reported that during colonoscopy polyps are detected in 40% of men and 30% of women (hyperplastic, adenomatous, serrated, among others), and, on average, 25% of adenomatous polyps (main quality indicator in colonoscopy). However, these lesions are not easy to observe due to the multiplicity of blind spots in the colon and the human error associated with the examination. Objective: to create a computational method for the automatic detection of colorectal polyps using artificial intelligence in recorded videos of real colonoscopy procedures. Methodology: public databases with colorectal polyps and a data collection built in a University Hospital were used. Initially, all the frames of the videos were normalized to reduce the high variability between databases. Subsequently, the polyp detection task is done with a deep learning method using a convolutional neural network. This network is initialized with weights learned on millions of national images from the ImageNet database. The weights of the network are updated using colonoscopy images, following the tuning technique.
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References
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Copyright (c) 2021 Martín Alonso Gómez-Zuleta, Diego Fernando Cano-Rosales, Diego Fernando Bravo-Higuera, Josué André Ruano-Balseca, Eduardo Romero-Castro
This work is licensed under a Creative Commons Attribution 4.0 International License.
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Prof. Zhigeng Pan
Director, Institute for Metaverse, Nanjing University of Information Science & Technology, China
Prof. Jianrong Tan
Academician, Chinese Academy of Engineering, China
Processing Speed (2023)
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- ＜7 days: submission to screening review decision
- 36 days: received to accepted
- 56 days: received to online
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