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However, retinal abnormalities due to hypertension are termed hypertensive retinopathy (HR). A severe stage of HR can lead to complete blindness if not diagnosed and treated on time. Manually analyzing retinal images for HR diagnosis is time\u2010consuming and prone to errors. This research article provides a novel technique based on U\u2010Net and Dense\u2010Net for automatic HR detection and grading through retinal images. The presented method consists of preprocessing, vessel segmentation, artery or vein (A\/V) classification, and vessel width calculation to compute the arteriovenous ratio (AVR). In the preprocessing phase, the Gabor filter is applied to the retinal image to enhance the vascular network of the image. The preprocessed image is fed into the U\u2010Net architecture to segment the vascular network image. The segmented vascular network image is fed into the Dense\u2010Net architecture for A\/V classification. The A\/V classified vascular network is divided into several artery and vein segments at the bifurcation and crossover points. The A\/V segments are labeled for width calculation to compute the AVR. The AVR is a standard parameter for HR detection and grading. The evaluation results show an average accuracy of 99.40% in HR classification and 99.77% in HR grading on the AVRDB dataset. The evaluated results are beneficial for the automatic HR detection and grading for clinical purposes.<\/jats:p>","DOI":"10.1002\/jemt.24847","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T12:09:44Z","timestamp":1742990984000},"page":"2333-2348","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Microscope\u2010Assisted Hypertensive Retinopathy Diagnosis Using Deep Learning Models"],"prefix":"10.1002","volume":"88","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6825-3851","authenticated-orcid":false,"given":"Shahzad","family":"Akbar","sequence":"first","affiliation":[{"name":"Riphah Artificial Intelligence Research (RAIR) Lab, Riphah College of Computing Riphah International University  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9320-0891","authenticated-orcid":false,"given":"Usama","family":"Shahzore","sequence":"additional","affiliation":[{"name":"Riphah Artificial Intelligence Research (RAIR) Lab, Riphah College of Computing Riphah International University  Faisalabad Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2579-4209","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab. CCIS Prince Sultan University  Riyadh Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0312-8731","authenticated-orcid":false,"given":"Faten S.","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Mathematical Sciences College of Science, Princess Nourah Bint Abdulrahman University  Riyadh Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sadaf S.","family":"Khan","sequence":"additional","affiliation":[{"name":"School of Medicine Shandong University  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":false,"given":"Amjad R.","family":"Khan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence &amp; Data Analytics Lab. 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