Automatic colon polyp detection using Convolutional Encoder-Decoder model

被引：0

作者：

Bardhi, Ornela ^{[1
]}

Sierra-Sosa, Daniel ^{[2
]}

Garcia-Zapirain, Begonya ^{[1
]}

Elmaghraby, Adel ^{[2
]}

机构：

[1] Univ Deusto, eVIDA Lab, Bilbao, Spain

[2] Univ Louisville, Comp Engn & Comp Sci Dept, Louisville, KY 40292 USA

来源：

2017 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT) | 2017年

基金：

欧盟地平线“2020”;

关键词：

deep learning; colon polyp detection; convolutional encoder-decoder; COLONOSCOPY;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Colorectal cancer is a leading cause of cancer deaths, estimated 696 thousand worldwide. Recent years have seen an increase of deep learning techniques and algorithms being used to detect colon polyps. In this work we address colon polyp detection using Convolutional Neural Networks (CNNs) combined with Autoencoders. We use 3 publicly available databases namely: CVC-ColonDB, CVC-ClinicDB and ETIS-LaribPolypDB, to train the model. The results obtained in terms of accuracy are: 0.937, 0.951, 0.967 for the above-mentioned databases respectively. Due to the nature of the colon polyps, diverse shapes and characteristics, there is still place for improvements.

引用

页码：445 / 448

页数：4

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