Classification of Polyps and Adenomas Using Deep Learning Model in Screening Colonoscopy
| Autor: | Ya Li, Xiaonan Yang, Xiaoda Liu, Jianning Yao, Jiayou Song, Bing Chen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
Colorectal cancer business.industry Diagnostic accuracy 02 engineering and technology Screening colonoscopy medicine.disease digestive system diseases Residual neural network 03 medical and health sciences 0302 clinical medicine Rectal epithelium 0202 electrical engineering electronic engineering information engineering medicine White light 030211 gastroenterology & hepatology 020201 artificial intelligence & image processing Radiology Detection rate business |
| Zdroj: | 2019 8th International Symposium on Next Generation Electronics (ISNE). |
| DOI: | 10.1109/isne.2019.8896649 |
| Popis: | Colorectal cancer (CRC) is the third leading cause of cancer-related death in China. It usually originates from the non-cancerous neoplasm polyps of the colon or rectal epithelium. Some polyps will evolve into precancerous lesions and eventually turn into colorectal cancer, Early screening and removal of adenomas can reduce the risk of colorectal cancer if screened. Unfortunately, more than 60% of colorectal cancer cases are attributed to missed polyps. Therefore, a deep learning network referred to as the faster_rcnn_inception_ resnet_v2 model was introduced for the localization and classification of precancerous lesions. It enables high-precision classification of polyps and adenomas under white light endoscopic images. The Mean Average Precision reached 90.645% when the Intersection over Union is set to 0.5. As an aid to clinicians, the model can improve the detection rate of adenomas and the diagnostic accuracy of early CRC. |
| Databáze: | OpenAIRE |
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