{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T08:59:58Z","timestamp":1782377998370,"version":"3.54.5"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T00:00:00Z","timestamp":1679356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s11042-023-15135-0","type":"journal-article","created":{"date-parts":[[2023,3,21]],"date-time":"2023-03-21T11:02:51Z","timestamp":1679396571000},"page":"36859-36884","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An automated diabetic retinopathy of severity grade classification using transfer learning and fine-tuning for fundus images"],"prefix":"10.1007","volume":"82","author":[{"given":"Sachin","family":"Chavan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Choubey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"key":"15135_CR1","doi-asserted-by":"crossref","unstructured":"Alzami F, Megantara RA, Fanani AZ (2019) Diabetic retinopathy grade classification based on fractal analysis and random forest. In2019 international seminar on application for Technology of Information and Communication (iSemantic) (pp. 272-276). IEEE","DOI":"10.1109\/ISEMANTIC.2019.8884217"},{"key":"15135_CR2","doi-asserted-by":"crossref","unstructured":"Bhatkar AP, Kharat GU (2015) Detection of diabetic retinopathy in retinal images using MLP classifier. In2015 IEEE international symposium on nanoelectronic and information systems. (pp. 331-335). IEEE","DOI":"10.1109\/iNIS.2015.30"},{"key":"15135_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.eswa.2018.07.026","volume":"114","author":"E Cetinic","year":"2018","unstructured":"Cetinic E, Lipic T, Grgic S (2018) Fine-tuning convolutional neural networks for fine art classification. Expert Syst Appl 114:107\u2013118","journal-title":"Expert Syst Appl"},{"key":"15135_CR4","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.diabres.2018.02.023","volume":"138","author":"N Cho","year":"2018","unstructured":"Cho N, Shaw JE, Karuranga S, Huang YD, da Rocha Fernandes JD, Ohlrogge AW, Malanda B (2018) IDF diabetes atlas: global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract 138:271\u2013281","journal-title":"Diabetes Res Clin Pract"},{"key":"15135_CR5","doi-asserted-by":"publisher","first-page":"18747","DOI":"10.1109\/ACCESS.2018.2816003","volume":"6","author":"P Costa","year":"2018","unstructured":"Costa P, Galdran A, Smailagic A, Campilho A (2018) A weakly-supervised framework for interpretable diabetic retinopathy detection on retinal images. IEEE Access 6:18747\u201318758","journal-title":"IEEE Access"},{"key":"15135_CR6","unstructured":"Cui X, Zhang W, T\u00fcske Z, Picheny M (2018) Evolutionary stochastic gradient descent for optimization of deep neural networks. arXiv preprint arXiv:1810.06773"},{"key":"15135_CR7","unstructured":"Daniel K, Michael G, Wenjia C et al (2018) Kaggle Dataset. https:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\/data"},{"key":"15135_CR8","doi-asserted-by":"publisher","first-page":"102600","DOI":"10.1016\/j.bspc.2021.102600","volume":"68","author":"S Das","year":"2021","unstructured":"Das S, Kharbanda K, Suchetha M, Raman R, Dhas E (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68:102600","journal-title":"Biomed Signal Process Control"},{"key":"15135_CR9","doi-asserted-by":"crossref","unstructured":"Doshi D, Shenoy A, Sidhpura D, Gharpure P (2016) Diabetic retinopathy detection using deep convolutional neural networks. In2016 international conference on computing, analytics and security trends (CAST) (pp. 261-266). IEEE","DOI":"10.1109\/CAST.2016.7914977"},{"issue":"3","key":"15135_CR10","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s13246-020-00890-3","volume":"43","author":"S Gayathri","year":"2020","unstructured":"Gayathri S, Gopi VP, Palanisamy P (2020) Automated classification of diabetic retinopathy through reliable feature selection. Phys Eng Sci Med 43(3):927\u2013945","journal-title":"Phys Eng Sci Med"},{"key":"15135_CR11","doi-asserted-by":"publisher","first-page":"57497","DOI":"10.1109\/ACCESS.2020.2979753","volume":"8","author":"S Gayathri","year":"2020","unstructured":"Gayathri S, Krishna AK, Gopi VP, Palanisamy P (2020) Automated binary and multiclass classification of diabetic retinopathy using haralick and multiresolution features. IEEE Access 8:57497\u201357504","journal-title":"IEEE Access"},{"key":"15135_CR12","doi-asserted-by":"publisher","first-page":"102115","DOI":"10.1016\/j.bspc.2020.102115","volume":"62","author":"S Gayathri","year":"2020","unstructured":"Gayathri S, Gopi VP, Palanisamy P (2020) A lightweight CNN for diabetic retinopathy classification from fundus images. Biomed Signal Process Control 62:102115","journal-title":"Biomed Signal Process Control"},{"key":"15135_CR13","doi-asserted-by":"crossref","unstructured":"Gayathri S, Gopi VP, Palanisamy P (2021) Diabetic retinopathy classification based on multipath CNN and machine learning classifiers.\u00a0Phys Eng Sci Med 44(3):639\u2013653","DOI":"10.1007\/s13246-021-01012-3"},{"key":"15135_CR14","doi-asserted-by":"publisher","first-page":"1432","DOI":"10.1016\/j.procs.2018.05.074","volume":"132","author":"A Gupta","year":"2018","unstructured":"Gupta A, Chhikara R (2018) Diabetic retinopathy: present and past. Procedia Comput Sci 132:1432\u20131440","journal-title":"Procedia Comput Sci"},{"issue":"3","key":"15135_CR15","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00521-018-03974-0","volume":"32","author":"DJ Hemanth","year":"2020","unstructured":"Hemanth DJ, Deperlioglu O, Kose U (2020) An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput & Applic 32(3):707\u2013721","journal-title":"Neural Comput & Applic"},{"key":"15135_CR16","volume-title":"A study on cnn transfer learning for image classification. InUK workshop on computational intelligence. (pp. 191-202)","author":"M Hussain","year":"2018","unstructured":"Hussain M, Bird JJ, Faria DR (2018) A study on cnn transfer learning for image classification. InUK workshop on computational intelligence. (pp. 191-202). Springer, Cham"},{"issue":"21","key":"15135_CR17","doi-asserted-by":"publisher","first-page":"15209","DOI":"10.1007\/s11042-018-7044-8","volume":"79","author":"U Ishtiaq","year":"2020","unstructured":"Ishtiaq U, Kareem SA, Abdullah ER, Mujtaba G, Jahangir R, Ghafoor HY (2020) Diabetic retinopathy detection through artificial intelligent techniques: a review and open issues. Multimed Tools Appl 79(21):15209\u201315252","journal-title":"Multimed Tools Appl"},{"key":"15135_CR18","doi-asserted-by":"crossref","unstructured":"Islam M, Dinh AV, Wahid KA (2017) Automated diabetic retinopathy detection using bag of words approach.\u00a0J Biomed Sci Eng 10(5):86\u201396","DOI":"10.4236\/jbise.2017.105B010"},{"key":"15135_CR19","doi-asserted-by":"publisher","first-page":"105320","DOI":"10.1016\/j.cmpb.2020.105320","volume":"191","author":"MM Islam","year":"2020","unstructured":"Islam MM, Yang HC, Poly TN, Jian WS, Li YC (2020) Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: a systematic review and meta-analysis. Comput Methods Prog Biomed 191:105320","journal-title":"Comput Methods Prog Biomed"},{"key":"15135_CR20","doi-asserted-by":"publisher","first-page":"163328","DOI":"10.1016\/j.ijleo.2019.163328","volume":"199","author":"TJ Jebaseeli","year":"2019","unstructured":"Jebaseeli TJ, Durai CA, Peter JD (2019) Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM. Optik. 199:163328","journal-title":"Optik"},{"key":"15135_CR21","doi-asserted-by":"crossref","unstructured":"Kandel I, Castelli M (2020) Transfer learning with convolutional neural networks for diabetic retinopathy image classification. A review. Appl Sci 10(6):2021","DOI":"10.3390\/app10062021"},{"key":"15135_CR22","doi-asserted-by":"crossref","unstructured":"Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R (2019) Diabetic retinopathy classification using a modified xception architecture. In2019 IEEE international symposium on signal processing and information technology (ISSPIT). IEEE,\u00a0pp 1\u20136","DOI":"10.1109\/ISSPIT47144.2019.9001846"},{"key":"15135_CR23","unstructured":"Krizhevsky A, et al. (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 1097\u20131105"},{"key":"15135_CR24","unstructured":"Lam C, et al. (2018) Automated detection of diabetic retinopathy using deep learning. AMIA summits on translational science proceedings. 147-155"},{"key":"15135_CR25","first-page":"147","volume":"2018","author":"C Lam","year":"2018","unstructured":"Lam C, Yi D, Guo M, Lindsey T (2018) Automated detection of diabetic retinopathy using deep learning. AMIA Summits Transl Sci Proceed 2018:147","journal-title":"AMIA Summits Transl Sci Proceed"},{"issue":"6","key":"15135_CR26","doi-asserted-by":"crossref","first-page":"4, 1","DOI":"10.1167\/tvst.8.6.41","volume":"8","author":"F Li","year":"2019","unstructured":"Li F et al (2019) Automatic detection of diabetic retinopathy in retinal fundus photographs based on deep learning algorithm. Translat Vis Sci Technol 8(6):4, 1\u20134,13","journal-title":"Translat Vis Sci Technol"},{"key":"15135_CR27","doi-asserted-by":"crossref","unstructured":"Li YH, Yeh NN, Chen SJ, Chung YC (2019) Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mob Inf Syst","DOI":"10.1155\/2019\/6142839"},{"issue":"5","key":"15135_CR28","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/s40846-018-0454-2","volume":"39","author":"N Memari","year":"2019","unstructured":"Memari N, Saripan MI, Mashohor S, Moghbel M (2019) Retinal blood vessel segmentation by using matched filtering and fuzzy c-means clustering with integrated level set method for diabetic retinopathy assessment. J Med Biol Eng 39(5):713\u2013731","journal-title":"J Med Biol Eng"},{"key":"15135_CR29","unstructured":"Messidor 2 Dataset (n.d.) https:\/\/www.adcis.net\/en\/third-party\/messidor2\/"},{"key":"15135_CR30","unstructured":"Messidor Dataset (n.d.) https:\/\/www.adcis.net\/en\/third-party\/messidor\/"},{"key":"15135_CR31","doi-asserted-by":"crossref","unstructured":"Mookiah MR, Acharya UR, Chua CK, Lim CM, Ng EY, Laude A (2013) Computer-aided diagnosis of diabetic retinopathy: a review.\u00a0Comput Biol Med 43(12):2136\u20132155","DOI":"10.1016\/j.compbiomed.2013.10.007"},{"key":"15135_CR32","unstructured":"Nair M, Mishra D (2019) Classification of diabetic retinopathy severity levels of transformed images using K-means and thresholding method.\u00a0Int J Eng Adv Technol 8(4):51\u201359"},{"key":"15135_CR33","doi-asserted-by":"crossref","unstructured":"Narasimhan K, Neha VC, Vijayarekha K (2012) An efficient automated system for detection of diabetic retinopathy from fundus images using support vector machine and bayesian classifiers. In2012 international conference on computing, electronics and electrical technologies (ICCEET). IEEE,\u00a0pp 964\u2013969","DOI":"10.1109\/ICCEET.2012.6203804"},{"key":"15135_CR34","doi-asserted-by":"crossref","unstructured":"Qiao L, Zhu Y, Zhou H (2020) Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access 8:104292\u2013104302","DOI":"10.1109\/ACCESS.2020.2993937"},{"key":"15135_CR35","doi-asserted-by":"crossref","unstructured":"Qomariah DU, Tjandrasa H, Fatichah C (2019) Classification of diabetic retinopathy and normal retinal images using CNN and SVM. In:\u00a02019 12th international conference on Information & Communication Technology and system (ICTS). IEEE,\u00a0pp 152\u2013157","DOI":"10.1109\/ICTS.2019.8850940"},{"issue":"3","key":"15135_CR36","first-page":"207","volume":"41","author":"Z Rahman","year":"2019","unstructured":"Rahman Z, Pu YF, Aamir M, Ullah F (2019) A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter. International. J Comput Appl 41(3):207\u2013217","journal-title":"J Comput Appl"},{"issue":"1","key":"15135_CR37","doi-asserted-by":"publisher","first-page":"24","DOI":"10.3390\/diagnostics10010024","volume":"10","author":"H Riaz","year":"2020","unstructured":"Riaz H, Park J, Choi H, Kim H, Kim J (2020) Deep and densely connected networks for classification of diabetic retinopathy. Diagnostics. 10(1):24","journal-title":"Diagnostics."},{"key":"15135_CR38","doi-asserted-by":"crossref","unstructured":"Roychowdhury A, Banerjee S (2018) Random forests in the classification of diabetic retinopathy retinal images. InAdvanced computational and communication paradigms:\u00a0Proceedings of International Conference on ICACCP 2017, vol 1. Springer, Singapore,\u00a0pp 168\u2013176","DOI":"10.1007\/978-981-10-8240-5_19"},{"issue":"4","key":"15135_CR39","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1016\/j.ophtha.2018.11.016","volume":"126","author":"R Sayres","year":"2019","unstructured":"Sayres R, Taly A, Rahimy E, Blumer K, Coz D, Hammel N, Krause J, Narayanaswamy A, Rastegar Z, Wu D, Xu S (2019) Using a deep learning algorithm and integrated gradients explanation to assist grading for diabetic retinopathy. Ophthalmology. 126(4):552\u2013564","journal-title":"Ophthalmology."},{"key":"15135_CR40","doi-asserted-by":"crossref","unstructured":"Seewoodhary M (2020) An overview of diabetic retinopathy and other ocular complications of diabetes mellitus. Eye","DOI":"10.7748\/ns.2021.e11696"},{"key":"15135_CR41","unstructured":"Selvathi D, Prakash NB, Balagopal N (n.d.) Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machine"},{"key":"15135_CR42","doi-asserted-by":"crossref","unstructured":"Shankar K, Perumal E, Vidhyavathi RM (2020) Deep neural network with moth search optimization algorithm based detection and classification of diabetic retinopathy images. SN Appl Sci 2:1\u20130","DOI":"10.1007\/s42452-020-2568-8"},{"key":"15135_CR43","doi-asserted-by":"crossref","unstructured":"Shanthi T, Sabeenian RS (2019) Modified Alexnet architecture for classification of diabetic retinopathy images.\u00a0Comput Electr Eng 76:56\u201364","DOI":"10.1016\/j.compeleceng.2019.03.004"},{"key":"15135_CR44","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1\u20139","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"15135_CR45","unstructured":"Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International conference on machine learning. PMLR,\u00a0pp 6105\u20136114"},{"key":"15135_CR46","unstructured":"Targ S, Almeida D, Lyman K (2016) Resnet in resnet: generalizing residual architectures. arXiv preprint arXiv:1603.08029"},{"key":"15135_CR47","doi-asserted-by":"publisher","unstructured":"Vijayan T, Sangeetha M, Kumaravel A, Karthik B (2020) Gabor filter and machine learning based diabetic retinopathy analysis and detection. Microprocess Microsyst 103353. https:\/\/doi.org\/10.1016\/j.micpro.2020.103353","DOI":"10.1016\/j.micpro.2020.103353"},{"key":"15135_CR48","doi-asserted-by":"publisher","first-page":"101936","DOI":"10.1016\/j.artmed.2020.101936","volume":"108","author":"Z Wu","year":"2020","unstructured":"Wu Z, Shi G, Chen Y, Shi F, Chen X, Coatrieux G, Yang J, Luo L, Li S (2020) Coarse-to-fine classification for diabetic retinopathy grading using convolutional neural network. Artif Intell Med 108:101936","journal-title":"Artif Intell Med"},{"key":"15135_CR49","doi-asserted-by":"crossref","unstructured":"Zaaboub N, Douik A (2020) Early diagnosis of diabetic retinopathy using random Forest algorithm. In:\u00a02020 5th international conference on advanced Technologies for Signal and Image Processing (ATSIP). IEEE,\u00a0pp 1\u20135","DOI":"10.1109\/ATSIP49331.2020.9231795"},{"key":"15135_CR50","doi-asserted-by":"publisher","first-page":"103537","DOI":"10.1016\/j.compbiomed.2019.103537","volume":"116","author":"GT Zago","year":"2020","unstructured":"Zago GT, Andre\u00e3o RV, Dorizzi B, Salles EO (2020) Diabetic retinopathy detection using red lesion localization and convolutional neural networks. Comput Biol Med 116:103537","journal-title":"Comput Biol Med"},{"key":"15135_CR51","doi-asserted-by":"publisher","first-page":"30744","DOI":"10.1109\/ACCESS.2019.2903171","volume":"7","author":"X Zeng","year":"2019","unstructured":"Zeng X, Chen H, Luo Y, Ye W (2019) Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7:30744\u201330753","journal-title":"IEEE Access"},{"issue":"10","key":"15135_CR52","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1038\/nrendo.2016.105","volume":"12","author":"P Zimmet","year":"2016","unstructured":"Zimmet P, Alberti KG, Magliano DJ, Bennett PH (2016) Diabetes mellitus statistics on prevalence and mortality: facts and fallacies. Nat Rev Endocrinol 12(10):616\u2013622","journal-title":"Nat Rev Endocrinol"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15135-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-15135-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-15135-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T09:28:29Z","timestamp":1696325309000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-15135-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,21]]},"references-count":52,"journal-issue":{"issue":"24","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["15135"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-15135-0","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,21]]},"assertion":[{"value":"4 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any studies with human participants and\/or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"There is no informed consent for this study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Authors declares that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}