Publication:
An Algorithm for Shrinking Blood Receptacles using Retinal Internal Pictures for Clinical Characteristics Measurement

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dc.contributor.authorAbdulsahib A.A.en_US
dc.contributor.authorMahmoud M.A.en_US
dc.contributor.authorAl-Hasnawi S.A.en_US
dc.contributor.authorid57222592694en_US
dc.contributor.authorid55247787300en_US
dc.contributor.authorid57962503400en_US
dc.date.accessioned2023-05-29T09:39:18Z
dc.date.available2023-05-29T09:39:18Z
dc.date.issued2022
dc.descriptionBlood vessels; Diagnosis; Information filtering; Ophthalmology; Photography; Characteristic measurements; Clinical characteristic measurement; Clinical characteristics; Filtering algorithm; Fundus photographs; Internal picture for retinal; Manual techniques; Morphological filtering; Morphological filtering algorithm; Segmentation vessel / shrinking blood receptacle; Blooden_US
dc.description.abstractThe manual technique that might use for shrinking vessels blood in the retinal fundus images has significant limitations, such as the high rate of time consumption and the possibility of human error, precisely appear with the sophisticated structure of the blood receptacle and a hung amount of the retinal fundus photograph that needs to be anatomic. Moreover, the automatic proposed algorithm that will utilize shrinking and explore helpful clinical characteristics from retinal fundus photographs in order to lead the eye caregiver to early diagnosis for various retinal disorders and therapy evaluations. A precise, quick, and fully-automatic algorithm for shrinking blood receptacles and clinical characteristics measuring technique for internal retinal pictures is suggested in order to increase the diagnostic accuracy and reduce the ophthalmologist's burden. The proposed algorithm's main pipeline consists of two fundamental stages: picture shrinkage and medical feature elicitation. Many exhaustive practices were conducted to evaluate the efficacy of the sophisticated fully-automated shrinkage system in figuring out retinal blood receptacles using the DRIVE and HRF datasets of exceedingly demanding fundus images. Initially, the accuracy of the created algorithm was tested based on its ability to accurately recognize the retinal structure of blood receptacles. In these attempts, several quantitative performance measures precisely five were computed to validate the efficacy of the exact algorithm, including accuracy (Acc.), sensitivity (Sen.), specificity (Spe.), positive prediction value (PPV), and negative prediction value (NPV). When contrast with modern receptacles shrinking approaches on the DRIVE dataset, the produced results have enormously improved by obtaining accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 98.78%, 98.32%, 97.23%, and 90. Based on five quantitative performance indicators, the HRF dataset led to the following results: 98.76%, 98.87%, 99.17%, 96.88%, and 100%. � 2022, International Journal of Advanced Computer Science and Applications. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.14569/IJACSA.2022.0131056
dc.identifier.epage488
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85141789005
dc.identifier.spage475
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85141789005&doi=10.14569%2fIJACSA.2022.0131056&partnerID=40&md5=74fa75ad0ba4b08a362583ce9d4aeb21
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27073
dc.identifier.volume13
dc.publisherScience and Information Organizationen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleInternational Journal of Advanced Computer Science and Applications
dc.titleAn Algorithm for Shrinking Blood Receptacles using Retinal Internal Pictures for Clinical Characteristics Measurementen_US
dc.typeArticleen_US
dspace.entity.typePublication
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