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The artery extraction layers encompass vessel-specific convolutional blocks to focus the tiny blood vessels and dense layers with skip connections for feature propagation. Segmentation is associated with artery extraction layers via individual loss function. Blood vessel maps produced from individual loss functions are authenticated for performance. The proposed technique attains improved outcomes in terms of Accuracy (0.9834), Sensitivity (0.8553), and Specificity (0.9835) from DRIVE, STARE, and CHASE-DB1 datasets. The result demonstrates that the proposed A-DFCNN is capable of segmenting minute vessel bifurcation breakdowns during the training and testing phases.<\/jats:p>","DOI":"10.3233\/jifs-224229","type":"journal-article","created":{"date-parts":[[2023,1,24]],"date-time":"2023-01-24T12:48:16Z","timestamp":1674564496000},"page":"6413-6423","source":"Crossref","is-referenced-by-count":6,"title":["Attention aware fully convolutional deep learning model for retinal blood vessel segmentation"],"prefix":"10.1177","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4562-9876","authenticated-orcid":false,"given":"C.","family":"Gobinath","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, 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