{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:00:34Z","timestamp":1791824434592,"version":"4.3.4"},"reference-count":23,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,16]],"date-time":"2025-02-16T00:00:00Z","timestamp":1739664000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100022773","name":"Consejo Veracruzano de Investigaci\u00f3n Cient\u00edfica y Desarrollo Tecnol\u00f3gico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100022773","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"Conselho Nacional de Desenvolvimento Cient\u00edfico e Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["306607\/2023-9"],"award-info":[{"award-number":["306607\/2023-9"]}],"id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["MethodsX"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1016\/j.mex.2025.103232","type":"journal-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T11:27:03Z","timestamp":1739791623000},"page":"103232","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":9,"special_numbering":"C","title":["Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization"],"prefix":"10.1016","volume":"14","author":[{"given":"Indresh Kumar","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shruti","family":"Patil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Supriya","family":"Mahadevkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Awanish Kumar","family":"Mishra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel J.P.C.","family":"Rodrigues","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.mex.2025.103232_bib0001","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1007\/s11831-022-09862-0","article-title":"A systematic review on diabetic retinopathy detection using deep learning techniques","volume":"30","author":"Vij","year":"2023","journal-title":"Archiv. Comput. Method. Eng."},{"issue":"11","key":"10.1016\/j.mex.2025.103232_bib0002","doi-asserted-by":"crossref","first-page":"17321","DOI":"10.1007\/s11042-022-13837-5","article-title":"A survey on recent developments in diabetic retinopathy detection through integration of deep learning","volume":"82","author":"Agarwal","year":"2023","journal-title":"Multimed. Tools Appl."},{"issue":"7","key":"10.1016\/j.mex.2025.103232_bib0003","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.3390\/diagnostics12071607","article-title":"Diabetic retinopathy detection from fundus images of the eye using hybrid deep learning features","volume":"12","author":"Butt","year":"2022","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.mex.2025.103232_bib0004","article-title":"Minimized computations of deep learning technique for early diagnosis of diabetic retinopathy using IoT-based medical devices","volume":"2022","author":"Ayoub","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"issue":"2","key":"10.1016\/j.mex.2025.103232_bib0005","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1007\/s11831-022-09816-6","article-title":"A literature review of early-stage diabetic retinopathy detection using deep learning and evolutionary computing techniques","volume":"30","author":"Bhandari","year":"2023","journal-title":"Archiv. Comput. Method. Eng."},{"key":"10.1016\/j.mex.2025.103232_bib0006","series-title":"2022 first international conference on electrical, electronics, information and communication technologies (ICEEICT)","first-page":"1","article-title":"Diabetic retinopathy detection using deep learning methods","author":"Suganyadevi","year":"2022"},{"key":"10.1016\/j.mex.2025.103232_bib0007","series-title":"2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC)","first-page":"0168","article-title":"An enhanced segmentation and deep learning architecture for early diabetic retinopathy detection","author":"Maaliw","year":"2023"},{"key":"10.1016\/j.mex.2025.103232_bib0008","doi-asserted-by":"crossref","first-page":"107293","DOI":"10.1109\/ACCESS.2022.3209825","article-title":"A review on machine learning styles in computer vision\u2014Techniques and future directions","volume":"10","author":"Mahadevkar","year":"2022","journal-title":"IEEe Access."},{"key":"10.1016\/j.mex.2025.103232_bib0009","doi-asserted-by":"crossref","first-page":"e1769","DOI":"10.7717\/peerj-cs.1769","article-title":"A comparison of deep transfer learning backbone architecture techniques for printed text detection of different font styles from unstructured documents","volume":"10","author":"Mahadevkar","year":"2024","journal-title":"PeerJ Comput. Sci."},{"issue":"24","key":"10.1016\/j.mex.2025.103232_bib0010","doi-asserted-by":"crossref","first-page":"11970","DOI":"10.3390\/app112411970","article-title":"Diabetic retinopathy improved detection using deep learning","volume":"11","author":"Ayala","year":"2021","journal-title":"Appl. Sci."},{"key":"10.1016\/j.mex.2025.103232_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108176","article-title":"Mayfly optimization with deep learning enabled retinal fundus image classification model","volume":"102","author":"Gupta","year":"2022","journal-title":"Comput. Electric. Eng."},{"issue":"2","key":"10.1016\/j.mex.2025.103232_bib0012","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1504\/IJNVO.2022.127605","article-title":"Salp swarm optimisation with deep transfer learning enabled retinal fundus image classification model","volume":"27","author":"Gupta","year":"2022","journal-title":"Int. J. Network. Virtual Org."},{"key":"10.1016\/j.mex.2025.103232_bib0013","first-page":"1","article-title":"A novel vessel extraction technique for a three-way classification of diabetic retinopathy using cascaded classifier","author":"Ather","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.mex.2025.103232_bib0014","first-page":"1","article-title":"RLeU-Net: segmentation of blood vessels in retinal fundus images for Diabetic Retinopathy screening","author":"Addanki","year":"2024","journal-title":"Multimed. Tools Appl."},{"issue":"5","key":"10.1016\/j.mex.2025.103232_bib0015","article-title":"Blood vessel segmentation and classification for diabetic retinopathy grading using dandelion optimization algorithm with deep learning model","volume":"16","author":"Ramesh","year":"2023","journal-title":"Int. J. Intell. Eng. Syst.,"},{"key":"10.1016\/j.mex.2025.103232_bib0016","first-page":"1","article-title":"Multi-dimensional fuzzy based diabetic retinopathy detection in retinal images through deep CNN method","author":"Balasamy","year":"2024","journal-title":"Multimed. Tools Appl."},{"issue":"11","key":"10.1016\/j.mex.2025.103232_bib0017","doi-asserted-by":"crossref","first-page":"33573","DOI":"10.1007\/s11042-023-16923-4","article-title":"Diabetic retinopathy detection using EADBSC and improved dilated ensemble CNN-based classification","volume":"83","author":"Thomas","year":"2024","journal-title":"Multimed. Tools Appl."},{"issue":"7","key":"10.1016\/j.mex.2025.103232_bib0018","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1007\/s00521-023-09324-z","article-title":"A novel Adaptive Neural Network-based Laplacian of Gaussian (AnLoG) classification algorithm for detecting diabetic retinopathy with colour retinal fundus images","volume":"36","author":"Ramasamy","year":"2024","journal-title":"Neural Comput. Appl."},{"issue":"5","key":"10.1016\/j.mex.2025.103232_bib0019","doi-asserted-by":"crossref","first-page":"7047","DOI":"10.1007\/s11042-022-11945-w","article-title":"Median filtering forensics based on optimum thresholding for low-resolution compressed images","volume":"81","author":"Agarwal","year":"2022","journal-title":"Multimed. Tools Appl."},{"issue":"2","key":"10.1016\/j.mex.2025.103232_bib0020","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1080\/00051144.2023.2295142","article-title":"Local search enhanced optimal Inception-ResNet-v2 for classification of long-term lung diseases in post-COVID-19 patients","volume":"65","author":"Sanampudi","year":"2024","journal-title":"Automatika"},{"issue":"16","key":"10.1016\/j.mex.2025.103232_bib0021","doi-asserted-by":"crossref","first-page":"4063","DOI":"10.3390\/en17164063","article-title":"Research on fault prediction of nuclear safety-class signal conditioning module based on improved GRU","volume":"17","author":"Chen","year":"2024","journal-title":"Energies. (Basel)"},{"issue":"8","key":"10.1016\/j.mex.2025.103232_bib0022","doi-asserted-by":"crossref","first-page":"699","DOI":"10.3390\/e26080699","article-title":"Multi-energy load prediction method for integrated energy system based on Fennec Fox optimization algorithm and Hybrid kernel extreme learning machine","volume":"26","author":"Shen","year":"2024","journal-title":"Entropy"},{"key":"10.1016\/j.mex.2025.103232_bib0023","unstructured":"https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/discussion\/234309."}],"container-title":["MethodsX"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016125000792?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2215016125000792?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:16:18Z","timestamp":1778865378000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2215016125000792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":23,"alternative-id":["S2215016125000792"],"URL":"https:\/\/doi.org\/10.1016\/j.mex.2025.103232","relation":{},"ISSN":["2215-0161"],"issn-type":[{"value":"2215-0161","type":"print"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Retinal fundus imaging-based diabetic retinopathy classification using transfer learning and fennec fox optimization","name":"articletitle","label":"Article Title"},{"value":"MethodsX","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.mex.2025.103232","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103232"}}