ECTI Transactions on Computer and Information Technology | |
Optimized transfer learning for polyp detection | |
article | |
Noppakun Boonsim1  Saranya Kanjaruek1  | |
[1] Khon Kaen University | |
关键词: Polyp detection; Transfer learning; InceptionRestnetV2; | |
DOI : 10.37936/ecti-cit.2023171.250910 | |
学科分类:医学(综合) | |
来源: Electrical Engineering/Electronics, Computer, Communications and Information Technology Association | |
【 摘 要 】
Early diagnosis of colorectal cancer focuses on detecting polyps in the colon as early as possible so that patients can have the best chances for success- ful treatment. This research presents the optimized parameters for polyp detection using a deep learning technique. Polyp and non-polyp images are trained on the InceptionResnetV2 model by the Faster Region Con- volutional Neural Networks (Faster R-CNN) framework to identify polyps within the colon images. The proposed method revealed more remarkable results than previous works, precision: 92.9 %, recall: 82.3%, F1-Measure: 87.3%, and F2-Measure: 54.6% on public ETIS-LARIB data set. This detection technique can reduce the chances of missing polyps during a pro- longed clinical inspection and can improve the chances of detecting multiple polyps in colon images.
【 授权许可】
CC BY-NC-ND
【 预 览 】
Files | Size | Format | View |
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RO202307090004826ZK.pdf | 999KB | download |