Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Jaehoon | - |
dc.contributor.author | Hong, Seung Taek | - |
dc.contributor.author | Ullah, Ihsan | - |
dc.contributor.author | Kim, Eun Sun | - |
dc.contributor.author | Park, Sang Hyun | - |
dc.date.accessioned | 2022-09-24T16:40:52Z | - |
dc.date.available | 2022-09-24T16:40:52Z | - |
dc.date.created | 2022-09-23 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 2075-4418 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/143905 | - |
dc.description.abstract | Confocal microscopy image analysis is a useful method for neoplasm diagnosis. Many ambiguous cases are difficult to distinguish with the naked eye, thus leading to high inter-observer variability and significant time investments for learning this method. We aimed to develop a deep learning-based neoplasm classification model that classifies confocal microscopy images of 10x magnified colon tissues into three classes: neoplasm, inflammation, and normal tissue. ResNet50 with data augmentation and transfer learning approaches was used to efficiently train the model with limited training data. A class activation map was generated by using global average pooling to confirm which areas had a major effect on the classification. The proposed method achieved an accuracy of 81%, which was 14.05% more accurate than three machine learning-based methods and 22.6% better than the predictions made by four endoscopists. ResNet50 with data augmentation and transfer learning can be utilized to effectively identify neoplasm, inflammation, and normal tissue in confocal microscopy images. The proposed method outperformed three machine learning-based methods and identified the area that had a major influence on the results. Inter-observer variability and the time required for learning can be reduced if the proposed model is used with confocal microscopy image analysis for diagnosis. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.subject | LASER ENDOMICROSCOPY | - |
dc.subject | BOWEL-DISEASE | - |
dc.subject | ARTIFICIAL-INTELLIGENCE | - |
dc.subject | CANCER | - |
dc.subject | ENDOSCOPY | - |
dc.subject | DIAGNOSIS | - |
dc.subject | RISK | - |
dc.subject | COLONOSCOPY | - |
dc.subject | PREVENTION | - |
dc.subject | NEOPLASIA | - |
dc.title | Classification of the Confocal Microscopy Images of Colorectal Tumor and Inflammatory Colitis Mucosa Tissue Using Deep Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Eun Sun | - |
dc.identifier.doi | 10.3390/diagnostics12020288 | - |
dc.identifier.scopusid | 2-s2.0-85124333047 | - |
dc.identifier.wosid | 000762518200001 | - |
dc.identifier.bibliographicCitation | DIAGNOSTICS, v.12, no.2 | - |
dc.relation.isPartOf | DIAGNOSTICS | - |
dc.citation.title | DIAGNOSTICS | - |
dc.citation.volume | 12 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | General & Internal Medicine | - |
dc.relation.journalWebOfScienceCategory | Medicine, General & Internal | - |
dc.subject.keywordPlus | LASER ENDOMICROSCOPY | - |
dc.subject.keywordPlus | BOWEL-DISEASE | - |
dc.subject.keywordPlus | ARTIFICIAL-INTELLIGENCE | - |
dc.subject.keywordPlus | CANCER | - |
dc.subject.keywordPlus | ENDOSCOPY | - |
dc.subject.keywordPlus | DIAGNOSIS | - |
dc.subject.keywordPlus | RISK | - |
dc.subject.keywordPlus | COLONOSCOPY | - |
dc.subject.keywordPlus | PREVENTION | - |
dc.subject.keywordPlus | NEOPLASIA | - |
dc.subject.keywordAuthor | colorectal neoplasm | - |
dc.subject.keywordAuthor | colorectal inflammation | - |
dc.subject.keywordAuthor | confocal microscopy | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | machine learning | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.