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    A Comparative Study on Polyp Classification and Localization from Colonoscopy Videos

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    A Comparative Study on Polyp Classification and Localization from Colonoscopy Videos Presentation (16.76Mb)
    Issue Date
    2019-08-31
    Author
    Fathan, Mohammad Isyroqi
    Publisher
    University of Kansas
    Format
    100 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
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    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.
    URI
    http://hdl.handle.net/1808/29702
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3828]

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    785-864-8983
    KU Libraries
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    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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