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Enhanced Approach for the Classification of Ulcerative Colitis Severity in Colonoscopy Videos Using CNN

Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To achieve the therapeutic goals of UC, which are to first induce and then maintain disease remission, doctors need to evaluate the severity of UC of a patient. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, in our previous works, we developed two different approaches in which one is using the image textures, and the other is using CNN (convolutional neural network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. But, we found that the image texture based approach could not handle larger number of variations in their patterns, and the CNN based approach could not achieve very high accuracy. In this paper, we improve our CNN based approach in two ways to provide better accuracy for the classification. We add more thorough and essential preprocessing, and generate more classes to accommodate large variations in their patterns. The experimental results show that the proposed preprocessing can improve the overall accuracy of evaluating the severity of UC.

Identiferoai:union.ndltd.org:unt.edu/info:ark/67531/metadc1538703
Date08 1900
CreatorsSure, Venkata Leela
ContributorsOh, JungHwan, Guo, Xuan, Do, Hyunsook
PublisherUniversity of North Texas
Source SetsUniversity of North Texas
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation
Formatvi, 23 pages, Text
RightsUse restricted to UNT Community, Sure, Venkata Leela, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved.

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