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These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning\u2010based automated system that detects and grades the papilledema through U\u2010Net and Dense\u2010Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense\u2010Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense\u2010Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U\u2010Net to achieve the segmented vascular network from which the vessel discontinuity index (VDI) and vessel discontinuity index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense\u2010Net are much better in terms of sensitivity 98.63%, specificity 97.83%, and accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U\u2010Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning\u2010based automated detection and grading of papilledema for clinical purposes is first effort in state of art.<\/jats:p>","DOI":"10.1002\/jemt.23865","type":"journal-article","created":{"date-parts":[[2021,7,8]],"date-time":"2021-07-08T11:06:12Z","timestamp":1625742372000},"page":"3066-3077","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Automatic detection of papilledema through fundus retinal images using deep learning"],"prefix":"10.1002","volume":"84","author":[{"given":"Tanzila","family":"Saba","sequence":"first","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics (AIDA) Lab CCIS Prince Sultan University  Riyadh 11586 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahzad","family":"Akbar","sequence":"additional","affiliation":[{"name":"Department of Computing Riphah International University, Faisalabad Campus  Faisalabad 38000 Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoshang","family":"Kolivand","sequence":"additional","affiliation":[{"name":"School of Computer Science and Mathematics Liverpool John Moores University  Liverpool United Kingdom"},{"name":"School of Computing and Digital Technologies, Staffordshire University  Staffordshire United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saeed","family":"Ali Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department College of Business Administration, Prince Sattam bin Abdulaziz University  Al\u2010Kharj Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,7,8]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2019.05.015"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2015.05.002"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.06.004"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-017-0712-9"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.11.014"},{"key":"e_1_2_10_7_1","unstructured":"Akbar S. 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