Publication:
Automatic diabetic retinopathy detection and classification system

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dc.Conferencecode132986
dc.Conferencedate2 October 2017 through 3 October 2017
dc.Conferencename7th IEEE International Conference on System Engineering and Technology, ICSET 2017
dc.citedby6
dc.contributor.affiliationsUniversiti Putra Malaysia (UPM)
dc.contributor.affiliationsUniversiti Sains Islam Malaysia (USIM)
dc.contributor.authorOmar Z.A.en_US
dc.contributor.authorHanafi M.en_US
dc.contributor.authorMashohor S.en_US
dc.contributor.authorMahfudz N.F.M.en_US
dc.contributor.authorMuna'Im M.en_US
dc.date.accessioned2024-05-28T08:24:54Z
dc.date.available2024-05-28T08:24:54Z
dc.date.issued2017
dc.description.abstractDiabetic Retinopathy (DR) is an eye disease due to diabetes, which is the most ordinary cause of blindness among adults of working age in Malaysia. To date, DR is still screened manually by ophthalmologist using fundus images due to insufficiently reliable existing automated DR detection systems. However, the manual screening process is the weakest link as it is a complicated and time-consuming process. Hence, this paper proposed an algorithm that consists of DR detection method with the aim to improve the accuracy of the existing systems. The methods used to detect DR features, namely exudates, hemorrhages and blood vessels can be categorized into several stages which are image pre-processing, vessel and hemorrhages detection, optic disc removal and exudate detection. However, the detection for blood vessel and hemorrhages was performed simultaneously due to similar intensity characteristics. The proposed algorithm was trained and tested using 49 and 89 fundus images, respectively. The images used in training were obtained from Hospital Serdang, Malaysia while images used in the testing were obtained from DIARETDB1 database. All of the images were categorized into four DR stages, namely mild Non-Proliferative Diabetic Retinopathy (NPDR), moderate NPDR, severe NPDR and Proliferative Diabetic Retinopathy (PDR). The images were captured under various illumination conditions. In the testing, the result shows that the percentage of detection for blood vessel and hemorrhages, and exudates are 98% and 100%, respectively. � 2017 IEEE.
dc.description.natureFinalen_US
dc.identifier.ArtNo8123439
dc.identifier.doi10.1109/ICSEngT.2017.8123439
dc.identifier.epage166
dc.identifier.isbn9781540000000
dc.identifier.scopus2-s2.0-85041429474
dc.identifier.spage162
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041429474&doi=10.1109%2fICSEngT.2017.8123439&partnerID=40&md5=093b1f0e2935df32ee59d92e7de3c4d8
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/8579
dc.languageEnglish
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2017 7th IEEE International Conference on System Engineering and Technology, ICSET 2017 - Proceedings
dc.sourceScopus
dc.subjectblood vesselsen_US
dc.subjectdetectionen_US
dc.subjectDiabetic Retinopathy (DR)en_US
dc.subjectexudatesen_US
dc.subjectfundus imagesen_US
dc.subjecthemorrhagesen_US
dc.subjectmicroaneurysmsen_US
dc.subjectpre-processingen_US
dc.subjectDiagnosisen_US
dc.subjectError detectionen_US
dc.subjectEye protectionen_US
dc.subjectImage processingen_US
dc.subjectSystems engineeringen_US
dc.subjectDiabetic retinopathyen_US
dc.subjectexudatesen_US
dc.subjectFundus imageen_US
dc.subjecthemorrhagesen_US
dc.subjectMicroaneurysmsen_US
dc.subjectPre-processingen_US
dc.subjectBlood vesselsen_US
dc.titleAutomatic diabetic retinopathy detection and classification system
dc.typeConference Paperen_US
dspace.entity.typePublication

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