{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T13:29:32Z","timestamp":1781357372261,"version":"3.54.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T00:00:00Z","timestamp":1699833600000},"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"],"DOI":"10.1007\/s11042-023-17462-8","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T08:02:01Z","timestamp":1699862521000},"page":"52253-52273","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2333-6968","authenticated-orcid":false,"given":"P.","family":"Saranya","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. M.","family":"Umamaheswari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,13]]},"reference":[{"key":"17462_CR1","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.compeleceng.2018.07.042","volume":"72","author":"S Wan","year":"2018","unstructured":"Wan S, Liang Y, Zhang Y (2018) Deep convolutional Neural Networks for Diabetic Retinopathy Detection by Image Classification. Comput Electr Eng 72:274\u2013282","journal-title":"Comput Electr Eng"},{"key":"17462_CR2","doi-asserted-by":"crossref","unstructured":"Wang L, Chen Z, Wang M, Wang T, Zhu W, Chen X (2021) \u201cCycle Adaptive Multi-Target Weighting Network for Automated Diabetic Retinopathy Segmentation,\u201d\u00a0IEEE 18th International Symposium on Biomedical Imaging (ISBI), pp. 1141\u20131144, 2021","DOI":"10.1109\/ISBI48211.2021.9433917"},{"key":"17462_CR3","doi-asserted-by":"crossref","unstructured":"Vashist P, Singh S, Gupta N, Saxena R (2011) Role of early screening for diabetic retinopathy in patients with diabetes mellitus: An overview. Indian J Commun Med Off Publication Indian Assoc. Prevent Soc Med 36(4):247","DOI":"10.4103\/0970-0218.91324"},{"issue":"1","key":"17462_CR4","doi-asserted-by":"publisher","first-page":"26","DOI":"10.4103\/0301-4738.178140","volume":"64","author":"C Valverde","year":"2016","unstructured":"Valverde C, Garcia M, Hornero R, Lopez-Galvez MI (2016) Automated detection of diabetic retinopathy in retinal images. Indian J Ophthalmol. 64(1):26\u201332","journal-title":"Indian J Ophthalmol."},{"key":"17462_CR5","first-page":"65","volume":"4","author":"S. Guptha Nirmala","year":"2014","unstructured":"Nirmala S. Guptha, Thanuja K (2014) \u201cWireless Technology to Monitor Remote Patients-A Survey,\u201d International Journal of Computer Networking. Wireless Mobile Commun (IJCNWMC) 4:65\u201376","journal-title":"Wireless Mobile Commun (IJCNWMC)"},{"key":"17462_CR6","doi-asserted-by":"crossref","unstructured":"Ahmed, Syed Thouheed S, Thanuja, Guptha NS, Narasimha S (2016) \"Telemedicine approach for remote patient monitoring system using smart phones with an economical hardware kit.\" In 2016 international conference on computing technologies and intelligent data engineering (ICCTIDE'16), 1\u20134","DOI":"10.1109\/ICCTIDE.2016.7725324"},{"issue":"2","key":"17462_CR7","first-page":"256","volume":"11","author":"NS Guptha","year":"2018","unstructured":"Guptha NS (2018) KK Patil,\"Detection of macro and micro nodule using online region based-active contour model in histopathological liver cirrhosis\u201d. Int J Intell Eng Syst 11(2):256\u2013265","journal-title":"Int J Intell Eng Syst"},{"issue":"7","key":"17462_CR8","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1049\/iet-ipr.2011.0333","volume":"6","author":"M Esmaeili","year":"2012","unstructured":"Esmaeili M, Rabbani H, Dehnavi AM, Dehghani A (2012) Automatic detection of Exudates and optic disk in retinal images using curvelet transform. IET Image Proc 6(7):1005\u20131013","journal-title":"IET Image Proc"},{"key":"17462_CR9","doi-asserted-by":"publisher","first-page":"176912","DOI":"10.1109\/ACCESS.2019.2957776","volume":"7","author":"X Guo","year":"2019","unstructured":"Guo X, Lu X, Liu Q, Che X (2019) EMFN: Enhanced Multi-Feature Fusion Network for Hard Exudate Detection in Fundus Images. IEEE Access 7:176912\u2013176920","journal-title":"IEEE Access"},{"key":"17462_CR10","doi-asserted-by":"publisher","first-page":"11946","DOI":"10.1109\/ACCESS.2018.2890426","volume":"7","author":"K Wisaeng","year":"2019","unstructured":"Wisaeng K, Sa-Ngiamvibool W (2019) Exudates Detection using Morphology Mean Shift Algorithm in Retinal Images. IEEE Access 7:11946\u201311958","journal-title":"IEEE Access"},{"key":"17462_CR11","doi-asserted-by":"publisher","first-page":"17077","DOI":"10.1109\/ACCESS.2017.2740239","volume":"5","author":"W Zhou","year":"2017","unstructured":"Zhou W, Wu C, Yi Y, Du W (2017) Automatic Detection of Exudates in Digital Color Fundus Images using Superpixel Multi-Feature Classification. IEEE Access 5:17077\u201317088","journal-title":"IEEE Access"},{"key":"17462_CR12","doi-asserted-by":"crossref","unstructured":"Wang H, Yuan G, Zhao X, Peng L, Wang Z, He Y, Qu C, Peng Z (2020) Hard Exudate Detection Based on Deep Model Learned Information and Multi-Feature Joint Representation for Diabetic Retinopathy Screening. Computer Methods and Programs in Biomedicine 191","DOI":"10.1016\/j.cmpb.2020.105398"},{"key":"17462_CR13","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.eswa.2018.07.053","volume":"114","author":"K Adem","year":"2018","unstructured":"Adem K (2018) Exudate detection for diabetic retinopathy with circular Hough transformation and convolutional neural networks. Expert Syst Appl 114:289\u2013295","journal-title":"Expert Syst Appl"},{"key":"17462_CR14","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cmpb.2016.09.018","volume":"137","author":"P Prentasic","year":"2016","unstructured":"Prentasic P, Loncaric S (2016) Detection of Exudates in Fundus Photographs using Deep Neural Networks and Anatomical Landmark Detection Fusion. Comput Methods Programs Biomed 137:281\u2013292","journal-title":"Comput Methods Programs Biomed"},{"key":"17462_CR15","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.1016\/j.procs.2020.03.287","volume":"167","author":"W Auccahuasi","year":"2020","unstructured":"Auccahuasi W, Flores E, Sernaque F, Cueva J, Diaz M et al (2020) Recognition of Hard Exudates using Deep Learning. Procedia Computer Science 167:2343\u20132353","journal-title":"Procedia Computer Science"},{"issue":"2","key":"17462_CR16","doi-asserted-by":"publisher","first-page":"509","DOI":"10.32604\/csse.2022.021909","volume":"42","author":"RS Rajkumar","year":"2022","unstructured":"Rajkumar RS, Selvarani AG (2022) Diabetic Retinopathy Diagnosis using ResNet with Fuzzy Rough C-Means Clustering. Comput Syst Sci Eng 42(2):509\u2013521","journal-title":"Comput Syst Sci Eng"},{"issue":"5","key":"17462_CR17","first-page":"214","volume":"8","author":"K SowmyaSundari","year":"2019","unstructured":"SowmyaSundari K, Guptha LK, Shruthi NS, Thanuja G, Anitha K (2019) Detection of liver lesion using ROBUST machine learning technique. Int J Eng Adv Technol (IJEAT) 8(5):214\u2013219","journal-title":"Int J Eng Adv Technol (IJEAT)"},{"key":"17462_CR18","doi-asserted-by":"crossref","unstructured":"Ahmed ST, SK S, Guptha NS, Lavanya NL, Basha SM, Fathima AS (2022) \u201cImproving Medical Image Pixel Quality Using Micq Unsupervised Machine Learning Technique.\u201d Malaysian J Comput Sci 53\u201364","DOI":"10.22452\/mjcs.sp2022no2.5"},{"issue":"1","key":"17462_CR19","first-page":"597","volume":"72","author":"S Sudha","year":"2022","unstructured":"Sudha S, Srinivasan A, Devi TG (2022) Detection and Classification of Diabetic Retinopathy using DCNN and BSN Models. CMC-Comput Mater Cont 72(1):597\u2013609","journal-title":"CMC-Comput Mater Cont"},{"issue":"9","key":"17462_CR20","doi-asserted-by":"publisher","first-page":"2352","DOI":"10.1162\/neco_a_00990","volume":"29","author":"W Rawat","year":"2017","unstructured":"Rawat W, Wang Z (2017) Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput 29(9):2352\u20132449","journal-title":"Neural Comput"},{"issue":"113","key":"17462_CR21","first-page":"1","volume":"6","author":"SS Yadav","year":"2019","unstructured":"Yadav SS, Jadhav SM (2019) Deep convolutional neural network based medical image classification for disease diagnosis. J Big Data 6(113):1\u201318","journal-title":"J Big Data"},{"key":"17462_CR22","doi-asserted-by":"crossref","unstructured":"Decenciere E, Zhang, Xiwei, Cazuguel G, Lay B, Cochener et al (2014) \u201cFeedback on a Publicly Distributed Image Database: The Messidor database.\u201d Image Anal Stereol","DOI":"10.5566\/ias.1155"},{"key":"17462_CR23","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","volume":"34","author":"E Decenciere","year":"2013","unstructured":"Decenciere E, Cazuguel G, Zhang X, Thibault G, Klein et al (2013) Teleophta: Machine Learning and Image Processing Methods for Teleophthalmology. IRBM 34:196\u2013203","journal-title":"IRBM"},{"key":"17462_CR24","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/978-981-13-9184-2_22","volume":"1036","author":"P Bannigidad","year":"2019","unstructured":"Bannigidad P, Deshpande A (2019) \u201cExudates Detection from Digital Fundus Images using GLCM Features with Decision Tree Classifier\u201d. Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2018. Communications in Computer and Information Science, Springer, Singapore 1036:245\u2013257","journal-title":"Communications in Computer and Information Science, Springer, Singapore"},{"issue":"3","key":"17462_CR25","first-page":"807","volume":"62","author":"RA Megantara","year":"2020","unstructured":"Megantara RA, Abdussalam Purwanto, Fanani AZ, Andono PN et al (2020) Exudates Detection for Multiclass Diabetic Retinopathy Grade Detection using Ensemble. Technol Reports Kansai Univ 62(3):807\u2013820","journal-title":"Technol Reports Kansai Univ"},{"issue":"7","key":"17462_CR26","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.3390\/sym14071427","volume":"14","author":"A Bilal","year":"2022","unstructured":"Bilal A, Zhu L, Deng A, Lu H, Wu N (2022) AI-Based Automatic Detection and Classification of Diabetic Retinopathy Using U-Net and Deep Learning. Symmetry 14(7):1427","journal-title":"Symmetry"},{"key":"17462_CR27","first-page":"78","volume":"4","author":"TM Usman","year":"2023","unstructured":"Usman TM, Saheed YK, Ignace D, Nsang A (2023) Diabetic retinopathy detection using principal component analysis multi-label feature extraction and classification. Int J Cogn Comput Eng 4:78\u201388","journal-title":"Int J Cogn Comput Eng"},{"issue":"1","key":"17462_CR28","doi-asserted-by":"publisher","first-page":"215","DOI":"10.13005\/bpj\/1366","volume":"11","author":"S Joshi","year":"2018","unstructured":"Joshi S, Karule PT (2018) Detection of Hard exudates Based on Morphological Feature Extraction. Biomed Pharmacol J 11(1):215\u2013225","journal-title":"Biomed Pharmacol J"},{"key":"17462_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/3926930","volume":"2019","author":"Shengchun Long","year":"2019","unstructured":"Long Shengchun, Huang Xiaoxiao, Chen Zhiqing, Pardhan Shahina, Zheng Dingchang (2019) Automatic Detection of Hard Exudates in Color Retinal Images using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation. BioMed Res Int 2019:1\u201313","journal-title":"BioMed Res Int"},{"issue":"4","key":"17462_CR30","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.3390\/s20041005","volume":"20","author":"A Colomer","year":"2020","unstructured":"Colomer A, Igual J, Naranjo V (2020) Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Sensors 20(4):1005","journal-title":"Sensors"},{"issue":"5","key":"17462_CR31","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1186\/s12859-021-04005-x","volume":"22","author":"PN Chen","year":"2021","unstructured":"Chen PN, Lee CC, Liang CM et al (2021) General deep learning model for detecting diabetic retinopathy. BMC Bioinformatics 22(5):84","journal-title":"BMC Bioinformatics"},{"key":"17462_CR32","unstructured":"Asiri NM, Hussain M, Adel FA, Aboalsamh (2022) \u201cA Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images.\u201d ArXiv,1\u201318"},{"key":"17462_CR33","doi-asserted-by":"publisher","first-page":"426","DOI":"10.1007\/s41315-022-00269-5","volume":"7","author":"A Malhi","year":"2023","unstructured":"Malhi A, Grewal R, Pannu HS (2023) Detection and diabetic retinopathy grading using digital retinal images. Int J Intell Robot Appl 7:426\u2013458","journal-title":"Int J Intell Robot Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17462-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17462-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17462-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T07:40:06Z","timestamp":1715758806000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17462-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,13]]},"references-count":33,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17462"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17462-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,13]]},"assertion":[{"value":"11 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 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 or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The Authors declare that there is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}