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A Comparative Study on Polyp Classification and Localization from Colonoscopy Videos
dc.contributor.advisor | Wang, Guanghui | |
dc.contributor.author | Fathan, Mohammad Isyroqi | |
dc.date.accessioned | 2019-11-01T00:59:41Z | |
dc.date.available | 2019-11-01T00:59:41Z | |
dc.date.issued | 2019-08-31 | |
dc.date.submitted | 2019 | |
dc.identifier.other | http://dissertations.umi.com/ku:16687 | |
dc.identifier.uri | http://hdl.handle.net/1808/29702 | |
dc.description.abstract | Colorectal cancer is one of the most common types of cancer with a high mortality rate. It typically develops from small clumps of benign cells called polyp. The adenomatous polyp has a higher chance of developing into cancer compared to the hyperplastic polyp. Colonoscopy is the preferred procedure for colorectal cancer screening and to minimize its risk by performing a biopsy on found polyps. Thus, a good polyp detection model can assist physicians and increase the effectiveness of colonoscopy. Several models using handcrafted features and deep learning approaches have been proposed for the polyp detection task. In this study, we compare the performances of the previous state-of-the-art general object detection models for polyp detection and classification (into adenomatous and hyperplastic class). Specifically, we compare the performances of FasterRCNN, SSD, YOLOv3, RefineDet, RetinaNet, and FasterRCNN with DetNet backbone. This comparative study serves as an initial analysis of the effectiveness of these models and to choose a base model that we will improve further for polyp detection. | |
dc.format.extent | 100 pages | |
dc.language.iso | en | |
dc.publisher | University of Kansas | |
dc.rights | Copyright held by the author. | |
dc.subject | Computer science | |
dc.subject | Colonoscopy | |
dc.subject | Computer Vision | |
dc.subject | Deep Learning | |
dc.subject | Machine Learning | |
dc.subject | Medical Imaging | |
dc.subject | Object Detection | |
dc.title | A Comparative Study on Polyp Classification and Localization from Colonoscopy Videos | |
dc.type | Thesis | |
dc.contributor.cmtemember | Miller, James R. | |
dc.contributor.cmtemember | Luo, Bo | |
dc.thesis.degreeDiscipline | Electrical Engineering & Computer Science | |
dc.thesis.degreeLevel | M.S. | |
dc.identifier.orcid | https://orcid.org/0000-0002-0398-8478 | |
dc.rights.accessrights | openAccess |
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Engineering Dissertations and Theses [1055]
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Theses [4088]