{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:39:51Z","timestamp":1773513591463,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Hypertensive retinopathy severity classification is proportionally related to tortuosity severity grading. No tortuosity severity scale enables a computer-aided system to classify the tortuosity severity of a retinal image. This work aimed to introduce a machine learning model that can identify the severity of a retinal image automatically and hence contribute to developing a hypertensive retinopathy or diabetic retinopathy automated grading system. First, the tortuosity is quantified using fourteen tortuosity measurement formulas for the retinal images of the AV-Classification dataset to create the tortuosity feature set. Secondly, a manual labeling is performed and reviewed by two ophthalmologists to construct a tortuosity severity ground truth grading for each image in the AV classification dataset. Finally, the feature set is used to train and validate the machine learning models (J48 decision tree, ensemble rotation forest, and distributed random forest). The best performance learned model is used as the tortuosity severity classifier to identify the tortuosity severity (normal, mild, moderate, and severe) for any given retinal image. The distributed random forest model has reported the highest accuracy (99.4%) compared to the J48 Decision tree model and the rotation forest model with minimal least root mean square error (0.0000192) and the least mean average error (0.0000182). The proposed tortuosity severity grading matched the ophthalmologist\u2019s judgment. Moreover, detecting the tortuosity severity of the retinal vessels\u2019, optimizing vessel segmentation, the vessel segment extraction, and the created feature set have increased the accuracy of the automatic tortuosity severity detection model.<\/jats:p>","DOI":"10.3390\/jimaging8100258","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T21:10:05Z","timestamp":1663881005000},"page":"258","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Four Severity Levels for Grading the Tortuosity of a Retinal Fundus Image"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0859-3299","authenticated-orcid":false,"given":"Sufian Abdul Qader","family":"Badawi","sequence":"first","affiliation":[{"name":"Department of Computing, School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad 44000, Pakistan"},{"name":"Center for Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9785-3920","authenticated-orcid":false,"given":"Maen","family":"Takruri","sequence":"additional","affiliation":[{"name":"Center for Information, Communication and Networking Education and Innovation (ICONET), American University of Ras Al Khaimah, Ras Al Khaimah 72603, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5785-5566","authenticated-orcid":false,"given":"Yaman","family":"Albadawi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, College of Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6140-1201","authenticated-orcid":false,"given":"Muazzam A. Khan","family":"Khattak","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Quaid-i-Azam University, Islamabad 45320, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9444-5398","authenticated-orcid":false,"given":"Ajay Kamath","family":"Nileshwar","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, RAK College of Medical Sciences, Ras Al Khaimah Campus, RAK Medical and Health Sciences University, Ras Al Khaimah 11172, United Arab Emirates"},{"name":"Saqr Hospital, Emirates Health Services, Ras Al Khaimah P.O. Box 5450, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1763-286X","authenticated-orcid":false,"given":"Emad","family":"Mosalam","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, RAK College of Medical Sciences, Ras Al Khaimah Campus, RAK Medical and Health Sciences University, Ras Al Khaimah 11172, United Arab Emirates"},{"name":"Dr. Emad Mosalam Eye Clinic, Ras Al Khaimah P.O. Box 5450, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"ref_1","unstructured":"Dictionary, O.E., and Idioms, E.U. (2022, May 19). Oxford. Dictionary-Tortuous. 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