Automatic Detection and Classification of Colorectal Polyps by Transferring Low-Level CNN Features From Nonmedical Domain
| Autor: | Carmen C. Y. Poon, Sunny H. Wong, Ruikai Zhang, Tony W. C. Mak, Ruoxi Yu, Yali Zheng, James Y.W. Lau |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Adenoma Colorectal cancer medicine.medical_treatment Colonic Polyps Magnification Colonoscopy 030218 nuclear medicine & medical imaging Machine Learning 03 medical and health sciences 0302 clinical medicine Health Information Management Image Interpretation Computer-Assisted medicine Humans Computer vision Electrical and Electronic Engineering Stage (cooking) medicine.diagnostic_test business.industry medicine.disease digestive system diseases Polypectomy Computer Science Applications Endoscopy ROC Curve Hyperplastic Polyp 030211 gastroenterology & hepatology Neural Networks Computer Radiology Artificial intelligence business Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 21:41-47 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2016.2635662 |
| Popis: | Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4-2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer. |
| Databáze: | OpenAIRE |
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