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dc.contributor.authorSalem, Nema
dc.contributor.authorNaveed, Khuram
dc.contributor.authorAkram, Awais
dc.contributor.authorAfaq, Amir
dc.contributor.authorMadni, Hussain
dc.contributor.authorKhan, Mohammad
dc.contributor.authordin, Mui
dc.contributor.authorRaza, Mohsin
dc.date.accessioned2023-03-16T04:44:20Z
dc.date.available2023-03-16T04:44:20Z
dc.date.issued2021-12-31
dc.identifier.citation4en_US
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0261698en_US
dc.identifier.urihttp://hdl.handle.net/20.500.14131/684
dc.description.abstractIn this era, deep learning-based medical image analysis has become a reliable source in assisting medical practitioners for various retinal disease diagnosis like hypertension, diabetic retinopathy (DR), arteriosclerosis glaucoma, and macular edema etc. Among these retinal diseases, DR can lead to vision detachment in diabetic patients which cause swelling of these retinal blood vessels or even can create new vessels. This creation or the new vessels and swelling can be analyzed as biomarker for screening and analysis of DR. Deep learning-based semantic segmentation of these vessels can be an effective tool to detect changes in retinal vasculature for diagnostic purposes. This segmentation task becomes challenging because of the low-quality retinal images with different image acquisition conditions, and intensity variations. Existing retinal blood vessels segmentation methods require a large number of trainable parameters for training of their networks. This paper introduces a novel Dense Aggregation Vessel Segmentation Network (DAVS-Net), which can achieve high segmentation performance with only a few trainable parameters. For faster convergence, this network uses an encoder-decoder framework in which edge information is transferred from the first layers of the encoder to the last layer of the decoder. Performance of the proposed network is evaluated on publicly available retinal blood vessels datasets of DRIVE, CHASE_DB1, and STARE. Proposed method achieved state-of-the-art segmentation accuracy using a few number of trainable parameters.en_US
dc.publisherPLOS ONEen_US
dc.titleDense Aggregation Vessel Segmentation Network for retinal vasculature detection in fundus imagesen_US
dc.source.journalPlos-oneen_US
dc.source.volume16en_US
dc.source.issue12en_US
dc.contributor.researcherExternal Collaborationen_US
dc.contributor.departmentElectrical and Computer Engineeringen_US
dc.contributor.firstauthorRaza, Mohsin


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