Transfer Learning with Convolutional Neural Network for Gastrointestinal Diseases Detection using Endoscopic Images

Jessica Escobar, N. Gomez, Karen Sanchez, H. Arguello
{"title":"Transfer Learning with Convolutional Neural Network for Gastrointestinal Diseases Detection using Endoscopic Images","authors":"Jessica Escobar, N. Gomez, Karen Sanchez, H. Arguello","doi":"10.1109/ColCACI50549.2020.9247847","DOIUrl":null,"url":null,"abstract":"Automated and accurate classification of pathologies on endoscopic images is a current challenge for Gastroenterology. This paper presents an approach to assist medical diagnosis processes of diseases and anomalies in the gastrointestinal tract based on the classification of features extracted from endoscopic images with a convolutional neural network and transfer learning type fine-tuning. The proposed strategy was evaluated on real endoscopic images from the Kvasir dataset. Specifically, we used 8000 images from 8 classes showing anatomical landmarks, pathological findings, and endoscopic procedures in the gastrointestinal tract. The proposed method allows obtaining an accuracy classification of 94.6%, which is 2.1% more accurate than the best result in the literature under very similar conditions, and up to 13.6% more precise.","PeriodicalId":446750,"journal":{"name":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ColCACI50549.2020.9247847","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

Automated and accurate classification of pathologies on endoscopic images is a current challenge for Gastroenterology. This paper presents an approach to assist medical diagnosis processes of diseases and anomalies in the gastrointestinal tract based on the classification of features extracted from endoscopic images with a convolutional neural network and transfer learning type fine-tuning. The proposed strategy was evaluated on real endoscopic images from the Kvasir dataset. Specifically, we used 8000 images from 8 classes showing anatomical landmarks, pathological findings, and endoscopic procedures in the gastrointestinal tract. The proposed method allows obtaining an accuracy classification of 94.6%, which is 2.1% more accurate than the best result in the literature under very similar conditions, and up to 13.6% more precise.
基于卷积神经网络的胃肠疾病内镜图像检测迁移学习
在内窥镜图像上的病理自动和准确分类是当前胃肠病学的一个挑战。本文提出了一种基于卷积神经网络和迁移学习类型微调对内镜图像提取的特征进行分类的方法,以辅助胃肠道疾病和异常的医学诊断过程。该策略在来自Kvasir数据集的真实内窥镜图像上进行了评估。具体来说,我们使用了8个类别的8000张图像,显示了胃肠道的解剖标志、病理发现和内窥镜手术。所提出的方法可以获得94.6%的准确率分类,比文献中在非常相似的条件下的最佳结果准确率提高2.1%,准确率提高13.6%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信