{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,4]],"date-time":"2026-10-04T04:53:43Z","timestamp":1791089623288,"version":"4.1.0"},"reference-count":74,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T00:00:00Z","timestamp":1635897600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Retinopathy of Prematurity (ROP) affects preterm neonates and could cause blindness. Deep Learning (DL) can assist ophthalmologists in the diagnosis of ROP. This paper proposes an automated and reliable diagnostic tool based on DL techniques called DIAROP to support the ophthalmologic diagnosis of ROP. It extracts significant features by first obtaining spatial features from the four Convolution Neural Networks (CNNs) DL techniques using transfer learning and then applying Fast Walsh Hadamard Transform (FWHT) to integrate these features. Moreover, DIAROP explores the best-integrated features extracted from the CNNs that influence its diagnostic capability. The results of DIAROP indicate that DIAROP achieved an accuracy of 93.2% and an area under receiving operating characteristic curve (AUC) of 0.98. Furthermore, DIAROP performance is compared with recent ROP diagnostic tools. Its promising performance shows that DIAROP may assist the ophthalmologic diagnosis of ROP.<\/jats:p>","DOI":"10.3390\/diagnostics11112034","type":"journal-article","created":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T17:59:38Z","timestamp":1635962378000},"page":"2034","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2657-2264","authenticated-orcid":false,"given":"Omneya","family":"Attallah","sequence":"first","affiliation":[{"name":"Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e20161978","DOI":"10.1542\/peds.2016-1978","article-title":"Trends in the screening and treatment of retinopathy of prematurity","volume":"139","author":"Vartanian","year":"2017","journal-title":"Pediatrics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1001\/archopht.125.11.1531","article-title":"Telemedical retinopathy of prematurity diagnosis: Accuracy, reliability, and image quality","volume":"125","author":"Chiang","year":"2007","journal-title":"Arch. 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