Lesions Multiclass Classification in Endoscopic Capsule Frames

被引:9
作者
Valerio, Maria Teresa [1 ,2 ]
Gomes, Sara [1 ,2 ]
Salgado, Marta [5 ]
Oliveira, Helder P. [1 ,4 ]
Cunha, Antonio [1 ,3 ]
机构
[1] INESC TEC, Porto, Portugal
[2] Univ Porto, Fac Engn, FEUP, Porto, Portugal
[3] UTAD Univ Tras Os Montes & Alto Douro, Vila Real, Portugal
[4] Univ Porto, Fac Ciencias, FCUP, Porto, Portugal
[5] Ctr Hosp Porto, Porto, Portugal
来源
CENTERIS2019--INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS/PROJMAN2019--INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT/HCIST2019--INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES | 2019年 / 164卷
关键词
Deep learning; transfer learning; capsule endoscopy; lesion detection;
D O I
10.1016/j.procs.2019.12.230
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Wireless capsule endoscopy is a relatively novel technique used for imaging of the gastrointestinal tract. Unlike traditional approaches, it allows painless visualisation of the whole of the gastrointestinal tract, including the small bowel, a region of difficult access. Endoscopic capsules record for about 8h, producing around 60,000 images. These are analysed by an expert that identifies abnormalities present in the frames, a process that is very tedious and prone to errors. Thus there is a clear need to develop systems that automatically analyse this data and detect lesions. Lesion detection achieved a precision of 0.94 and a recall of 0.93 by fmetuning the pre-trained DenseNet-161 model. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页码:637 / 645
页数:9
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