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It is not easily detected since there are no symptoms, but it can be detected using tonometry, ophthalmoscopy, and perimeter. However, advances in artificial intelligence approaches have permitted machine learning techniques to diagnose at an early stage. Numerous methods have been proposed using Machine Learning to diagnose glaucoma with different data sets and techniques but these are complex methods. Although, medical imaging instruments are used as glaucoma screening methods, fundus imaging specifically is the most used screening technique for glaucoma detection. This study presents a novel DenseNet and DarkNet combination to classify normal and glaucoma affected fundus image. These frameworks have been trained and tested on three data sets of high\u2010resolution fundus (HRF), RIM 1, and ACRIMA. A total of 658 images have been used for healthy eyes and 612 images for glaucoma\u2010affected eyes classification. It has also been observed that the fusion of DenseNet and DarkNet outperforms the two CNN networks and achieved 99.7% accuracy, 98.9% sensitivity, 100% specificity for the HRF database. In contrast, for the RIM1 database, 89.3% accuracy, 93.3% sensitivity, 88.46% specificity has been attained. Moreover, for the ACRIMA database, 99% accuracy, 100% sensitivity, 99% specificity has been achieved. Therefore, the proposed method is robust and efficient with less computational time and complexity compared to the literature available.<\/jats:p>","DOI":"10.1002\/jemt.24083","type":"journal-article","created":{"date-parts":[[2022,2,16]],"date-time":"2022-02-16T03:05:40Z","timestamp":1644980740000},"page":"2259-2276","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Detection of microscopic glaucoma through fundus images using deep transfer learning approach"],"prefix":"10.1002","volume":"85","author":[{"given":"Shahzad","family":"Akbar","sequence":"first","affiliation":[{"name":"Riphah College of Computing Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Ale","family":"Hassan","sequence":"additional","affiliation":[{"name":"Riphah College of Computing Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayesha","family":"Shoukat","sequence":"additional","affiliation":[{"name":"Riphah College of Computing Riphah International University, Faisalabad Campus  Faisalabad Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaber","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Faculty of Applied Medical Sciences King Abdulaziz University  Jeddah 21589 Saudi Arabia"},{"name":"Imaging Unit, King Fahd Medical Research Center King Abdulaziz University  Jeddah 21589 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3406-4320","authenticated-orcid":false,"given":"Saeed Ali","family":"Bahaj","sequence":"additional","affiliation":[{"name":"MIS Department, College of Business Administration Prince Sattam Bin Abdulaziz University  Alkharj 11942 Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2017.080606"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2474-6"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1049\/iet-ipr.2019.0036"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0207982"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2018.06.004"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-017-0712-9"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2017.11.014"},{"key":"e_1_2_10_9_1","unstructured":"Akbar S. Hassan T. Akram M. U. Yasin U. U. &Basit I.(2017).AVRDB: Annotated dataset for vessel segmentation and calculation of arteriovenous ratio. 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