eprintid: 868
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/08/68
datestamp: 2023-11-09 15:49:00
lastmod: 2023-11-09 15:49:00
status_changed: 2023-11-09 15:38:37
type: conference_item
metadata_visibility: show
creators_name: Hani, A.F.M.
creators_name: Nugroho, H.A.
creators_name: Nugroho, H.
title: Gaussian bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy
ispublished: pub
keywords: Bayes Classifier; Belong to; Classification process; Computerized systems; Correlation factors; Cross validation; diabetic retinopathy; Early detection; Foveal avascular zones; fundus image; Gaussians; Medical diagnosis; Monitoring system, Biomedical engineering; Diagnosis; Engineering research; Eye protection; Image enhancement; Medical imaging, Gaussian distribution
note: cited By 16; Conference of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010 ; Conference Date: 30 November 2010 Through 2 December 2010; Conference Code:84636
abstract: Data from medical imaging system need to be analysed for diagnostics and clinical purposes. In a computerized system, the analysis normally involves classification process to determine disease and its condition. In an earlier work based on a database of 315 fundus images (FINDeRS), it is found that the foveal avascular zone (FAZ) enlargement strongly correlates with diabetic retinopathy (DR) progression having a correlation factor up to 0.883 at significant levels better than 0.01. However, it is also found that the FAZ areas can belong to different DR severity but with different levels of certainty having a Gaussian distribution. In this research work, the suitability of the Gaussian Bayes classifier in determining DR severity level is investigated. A v-fold cross-validation (VFCF) process is applied to the FINDeRS database to evaluate the performance of the classifier. It is shown that the classifier achieved sensitivity of >84, specificity of >97 and accuracy of >95 for all DR stages. At high values of sensitivity (>95), specificity (>97) and accuracy (>98) obtained for No DR and Severe NPDR/PDR stages, the Gaussian Bayes classifier is suitable as part of a computerised DR grading and monitoring system for early detection of DR and for effective treatment of severe cases. © 2010 IEEE.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955449370&doi=10.1109%2fIECBES.2010.5742198&partnerID=40&md5=561548ad709151ae2b5c5ce6d1f711e8
id_number: 10.1109/IECBES.2010.5742198
full_text_status: none
publication: Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
place_of_pub: Kuala Lumpur
pagerange: 52-56
refereed: TRUE
isbn: 9781424476008
citation:   Hani, A.F.M. and Nugroho, H.A. and Nugroho, H.  (2010) Gaussian bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy.  In: UNSPECIFIED.