{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T15:35:30Z","timestamp":1705851330957},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684000","type":"print"},{"value":"9781643684017","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T00:00:00Z","timestamp":1687996800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,6,29]]},"abstract":"<jats:p>The optical disc in the human retina can reveal important information about a person\u2019s health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images. Using an attention-based residual U-Net, we showed that the optical disc in a human retina image can be detected with more than 99% pixel-level accuracy and around 95% in Matthew\u2019s Correlation Coefficient. A comparison with variants of UNet with different encoder CNN architectures ascertains the superiority of the proposed approach across multiple metrics.<\/jats:p>","DOI":"10.3233\/shti230576","type":"book-chapter","created":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:54:59Z","timestamp":1688111699000},"source":"Crossref","is-referenced-by-count":1,"title":["Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning"],"prefix":"10.3233","author":[{"given":"Mohammad Tariqul","family":"Islam","sequence":"first","affiliation":[{"name":"Southern Connecticut State University, Connecticut, USA"}]},{"given":"Ferdaus","family":"Ahmed","sequence":"additional","affiliation":[{"name":"Infosys Ltd, Texas, USA"}]},{"given":"Mowafa","family":"Househ","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad bin Khalifa University, Doha, Qatar"}]},{"given":"Tanvir","family":"Alam","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Hamad bin Khalifa University, Doha, Qatar"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Healthcare Transformation with Informatics and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI230576","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,30]],"date-time":"2023-06-30T07:55:00Z","timestamp":1688111700000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI230576"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,29]]},"ISBN":["9781643684000","9781643684017"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti230576","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,29]]}}}