A PHP Error was encountered

Severity: Warning

Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests

Filename: helpers/my_audit_helper.php

Line Number: 143

Backtrace:

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML

File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016

File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global

File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword

File: /var/www/html/index.php
Line: 316
Function: require_once

Evaluation of deep convolutional neural networks for glaucoma detection. | LitMetric

Evaluation of deep convolutional neural networks for glaucoma detection.

Jpn J Ophthalmol

Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan.

Published: May 2019

AI Article Synopsis

  • The study explored how well deep convolutional neural networks (DCNNs) can identify glaucoma using a dataset of 3,312 color fundus images, including those from confirmed, suspected, and non-glaucomatous eyes.
  • The results demonstrated that the DCNNs achieved high accuracy (AUCs above 0.9), with better performance observed when using images from confirmed glaucoma cases compared to suspected cases.
  • Additionally, the analysis indicated that image quality is crucial for performance, as including poor-quality images lowered the accuracy, while the image size did not significantly impact the ability to discriminate glaucoma.

Article Abstract

Purpose: To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images STUDY DESIGN: A retrospective study PATIENTS AND METHODS: To investigate the discriminative ability of 3 DCNNs, we used a total of 3312 images consisting of 369 images from glaucoma-confirmed eyes, 256 images from glaucoma-suspected eyes diagnosed by a glaucoma expert, and 2687 images judged to be nonglaucomatous eyes by a glaucoma expert. We also investigated the effects of image size on the discriminative ability and heatmap analysis to determine which parts of the image contribute to the discrimination. Additionally, we used 465 poor-quality images to investigate the effect of poor image quality on the discriminative ability.

Results: Three DCNNs showed areas under the curve (AUCs) of 0.9 or more. The AUC of the DCNN using glaucoma-confirmed eyes against nonglaucomatous eyes was higher than that using glaucoma-suspected eyes against nonglaucomatous eyes by approximately 0.1. The image size did not affect the discriminative ability. Heatmap analysis showed that the optic disc area was the most important area for the discrimination of glaucoma. The image quality affected the discriminative ability, and the inclusion of poor-quality images in the analysis reduced the AUC by 0.1 to 0.2.

Conclusions: DCNNs may be a useful tool for detecting glaucoma or glaucoma-suspected eyes by use of fundus color images. Proper preprocessing and collection of qualified images are essential to improving the discriminative ability.

Download full-text PDF

Source
http://dx.doi.org/10.1007/s10384-019-00659-6DOI Listing

Publication Analysis

Top Keywords

discriminative ability
20
glaucoma-suspected eyes
12
nonglaucomatous eyes
12
images
9
deep convolutional
8
convolutional neural
8
neural networks
8
eyes
8
glaucoma-confirmed eyes
8
glaucoma expert
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!