13 15

Cited 0 times in

Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database

Authors
 Joon Yul Choi  ;  Tae Keun Yoo  ;  Jeong Gi Seo  ;  Jiyong Kwak  ;  Terry Taewoong Um  ;  Tyler Hyungtaek Rim 
Citation
 PLoS One, Vol.12(11) : e0187336, 2017 
Journal Title
 PLoS One 
Issue Date
2017
MeSH
Databases, Factual* ; Diabetic Retinopathy/diagnostic imaging ; Humans ; Learning* ; Neural Networks (Computer)* ; Pilot Projects ; Retina/diagnostic imaging*
Abstract
Deep learning emerges as a powerful tool for analyzing medical images. Retinal disease detection by using computer-aided diagnosis from fundus image has emerged as a new method. We applied deep learning convolutional neural network by using MatConvNet for an automated detection of multiple retinal diseases with fundus photographs involved in STructured Analysis of the REtina (STARE) database. Dataset was built by expanding data on 10 categories, including normal retina and nine retinal diseases. The optimal outcomes were acquired by using a random forest transfer learning based on VGG-19 architecture. The classification results depended greatly on the number of categories. As the number of categories increased, the performance of deep learning models was diminished. When all 10 categories were included, we obtained results with an accuracy of 30.5%, relative classifier information (RCI) of 0.052, and Cohen's kappa of 0.224. Considering three integrated normal, background diabetic retinopathy, and dry age-related macular degeneration, the multi-categorical classifier showed accuracy of 72.8%, 0.283 RCI, and 0.577 kappa. In addition, several ensemble classifiers enhanced the multi-categorical classification performance. The transfer learning incorporated with ensemble classifier of clustering and voting approach presented the best performance with accuracy of 36.7%, 0.053 RCI, and 0.225 kappa in the 10 retinal diseases classification problem. First, due to the small size of datasets, the deep learning techniques in this study were ineffective to be applied in clinics where numerous patients suffering from various types of retinal disorders visit for diagnosis and treatment. Second, we found that the transfer learning incorporated with ensemble classifiers can improve the classification performance in order to detect multi-categorical retinal diseases. Further studies should confirm the effectiveness of algorithms with large datasets obtained from hospitals.
URI
http://ir.ymlib.yonsei.ac.kr/handle/22282913/161343
DOI
10.1371/journal.pone.0187336
Appears in Collections:
1. Journal Papers (연구논문) > 1. College of Medicine (의과대학) > Dept. of Ophthalmology (안과학교실)
Yonsei Authors
곽지용(Kwak, Jiyong) ; 임형택(Rim, Tyler Hyungtaek)
사서에게 알리기
  feedback
Files in This Item:
T201704610.pdf Download
Export
RIS (EndNote)
XLS (Excel)
XML

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse