{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T15:25:15Z","timestamp":1784906715626,"version":"3.55.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"33","license":[{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:00:00Z","timestamp":1743033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"NA","award":["NA"],"award-info":[{"award-number":["NA"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20703-7","type":"journal-article","created":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T03:37:18Z","timestamp":1743305838000},"page":"40905-40935","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Efficient segmentation of exudates in color fundus images using wavelets and generative adversarial network"],"prefix":"10.1007","volume":"84","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6326-4068","authenticated-orcid":false,"given":"Mithun Kumar","family":"Kar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1959-6452","authenticated-orcid":false,"given":"Malaya Kumar","family":"Nath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debanga Raj","family":"Neog","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,3,27]]},"reference":[{"issue":"12","key":"20703_CR1","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.1016\/j.compbiomed.2013.10.007","volume":"43","author":"MRK Mookiah","year":"2013","unstructured":"Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng E, Laude A (2013) Computer-aided diagnosis of diabetic retinopathy: A review. Comput Biol Med 43(12):2136\u20132155","journal-title":"Comput Biol Med"},{"key":"20703_CR2","first-page":"2136","volume":"10","author":"T Walter","year":"2002","unstructured":"Walter T, Klein J-C, Massin P, Erginay A (2002) A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Trans Med Imaging 10:2136\u20132155","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"20703_CR3","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.media.2009.05.005","volume":"13","author":"CI Sanchez","year":"2009","unstructured":"Sanchez CI, Garcia M, Mayo A, Lopez MI, Hornero R (2009) Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 13(4):650\u2013658","journal-title":"Med Image Anal"},{"issue":"2","key":"20703_CR4","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1046\/j.1464-5491.2002.00613.x","volume":"19","author":"C Sinthanayothin","year":"2002","unstructured":"Sinthanayothin C, Boyce JF, Williamson TH, Cook HL, Mensah E, Lal S, Usher D (2002) Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 19(2):105\u2013112","journal-title":"Diabet Med"},{"issue":"8","key":"20703_CR5","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.compmedimag.2008.08.009","volume":"32","author":"A Sopharak","year":"2008","unstructured":"Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720\u2013727","journal-title":"Comput Med Imaging Graph"},{"key":"20703_CR6","doi-asserted-by":"crossref","unstructured":"Priyanka R,\u00a0Aravinth J (2021) Comparative analysis of different machine learning classifiers for prediction of diabetic retinopathy. In: 2021 International Conference on Recent Trends on Electronics, Information, Communication and Technology (RTEICT), pp\u00a0233\u2013239","DOI":"10.1109\/RTEICT52294.2021.9573525"},{"key":"20703_CR7","doi-asserted-by":"crossref","unstructured":"Sivaranjani S,\u00a0Ananya S,\u00a0Aravinth J,\u00a0Karthika R (2021) Diabetes prediction using machine learning algorithms with feature selection and dimensionality reduction. In: 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), vol 1, pp 141\u2013146","DOI":"10.1109\/ICACCS51430.2021.9441935"},{"key":"20703_CR8","first-page":"130","volume-title":"Detection of exudates in fundus images using a markovian segmentation model. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","author":"B Harangi","year":"2016","unstructured":"Harangi B, Hajdu A (2016) Detection of exudates in fundus images using a markovian segmentation model. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Chicago, IL, USA, pp 130\u2013133"},{"key":"20703_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/3926930","volume":"2019","author":"S Long","year":"2019","unstructured":"Long S, Huang X, Chen Z, Pardhan S, Zheng D (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":"2000","key":"20703_CR10","first-page":"165","volume":"62","author":"BM Ege","year":"2018","unstructured":"Ege BM, Hejlesen OK, Larsen OV, M\u00f8ller K, Jennings B, Kerr D, Cavan DA (2018) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Methods Programs Biomed 62(2000):165\u2013175","journal-title":"Comput Methods Programs Biomed"},{"key":"20703_CR11","doi-asserted-by":"crossref","unstructured":"Garcia M, S\u00e1nchez CI, L\u00f3pez MI,\u00a0Ab\u00e1solo D,\u00a0Hornero R (2009) Neural network based detection of hard exudates in retinal images 93(1): 9\u201319","DOI":"10.1016\/j.cmpb.2008.07.006"},{"issue":"2017","key":"20703_CR12","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.bspc.2017.02.012","volume":"35","author":"MM Fraz","year":"2017","unstructured":"Fraz MM, Jahangir W, Zahida S, Hamayuna MM, Barman SA (2017) Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification. Biomed Signal Process Control 35(2017):50\u201362","journal-title":"Biomed Signal Process Control"},{"issue":"7","key":"20703_CR13","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1016\/j.media.2014.05.004","volume":"18","author":"X Zhang","year":"2014","unstructured":"Zhang X, Thibault G, Decenci\u00e8re E, Marcotegui B, La\u00ff B, Danno R, Cazuguel G, Quellec G, Lamard M, Massin P (2014) Exudate detection in color retinal images for mass screening of diabetic retinopathy. Med Image Anal 18(7):1026\u20131043","journal-title":"Med Image Anal"},{"issue":"2","key":"20703_CR14","first-page":"195","volume":"23","author":"G Mahendran","year":"2013","unstructured":"Mahendran G, Dhanasekaran R (2013) Detection and localization of retinal exudates for diabetic retinopathy. Int J Biomed Imaging 23(2):195\u2013211","journal-title":"Int J Biomed Imaging"},{"key":"20703_CR15","doi-asserted-by":"crossref","unstructured":"Benzamin A,\u00a0Chakraborty C (2018) Detection of hard exudates in retinal fundus images using deep learning. Joint 7th International Conference on Informatics, Electronics and Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision and Pattern Recognition (icIVPR), pp 465\u2013469","DOI":"10.1109\/ICIEV.2018.8641016"},{"issue":"2020","key":"20703_CR16","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.1016\/j.procs.2020.03.287","volume":"167","author":"W Auccahuasia","year":"2020","unstructured":"Auccahuasia W, Floresb E, Sernaqueb F, Cuevab J, Diazb M, Or\u00e9 E (2020) Recognition of hard exudates using deep learning. Procedia Computer Science 167(2020):2343\u20132353","journal-title":"Procedia Computer Science"},{"key":"20703_CR17","doi-asserted-by":"crossref","unstructured":"Yu S,\u00a0Xiao D,\u00a0Kanagasingam Y (2017) Exudate detection for diabetic retinopathy with convolutional neural networks, pp 1744\u20131747","DOI":"10.1109\/EMBC.2017.8037180"},{"key":"20703_CR18","doi-asserted-by":"crossref","unstructured":"V\u00a0S, K\u00a0S A, K\u00a0K\u00a0T R, U\u00a0M R, K\u00a0R (2021) Performance analysis of diabetic retinopathy classification using cnn. In: 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA), pp 823\u2013828","DOI":"10.1109\/ICIRCA51532.2021.9544730"},{"key":"20703_CR19","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.neucom.2018.02.035","volume":"290","author":"J Mo","year":"2018","unstructured":"Mo J, Zhang L, Feng Y (2018) Exudate-based diabetic macular edema recognition in retinal images using cascaded deep residual networks. Neurocomputing 290:161\u2013171","journal-title":"Neurocomputing"},{"key":"20703_CR20","doi-asserted-by":"crossref","unstructured":"Prenta\u0161i\u0107 P,\u00a0Lon\u010dari\u0107 S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion vol 137, pp 281\u2013292","DOI":"10.1016\/j.cmpb.2016.09.018"},{"key":"20703_CR21","first-page":"1","volume":"5801870","author":"M Mateen","year":"2020","unstructured":"Mateen M, Wen J, Nasrullah N, Sun S (2020) Hayat S (2020) Exudate detection for diabetic retinopathy using pretrained convolutional neural networks. Medical Image Analysis 5801870:1\u201311","journal-title":"Medical Image Analysis"},{"key":"20703_CR22","doi-asserted-by":"crossref","unstructured":"Decenci\u00e8re E,\u00a0Cazuguel G, XZ et\u00a0al (2013) Teleophta: Machine learning and image processing methods for teleophthalmology. IRBM, vol 34(2):196\u2013203","DOI":"10.1016\/j.irbm.2013.01.010"},{"key":"20703_CR23","doi-asserted-by":"crossref","unstructured":"KauppiValentina T, Kalesnykiene, J-KK et\u00a0al (2007) Diaretdb1 diabetic retinopathy database and evaluation protocol. Proceedings of the British Machine Vision Conference, pp 1\u201310","DOI":"10.5244\/C.21.15"},{"key":"20703_CR24","doi-asserted-by":"crossref","unstructured":"Khojasteha P, J\u00faniorb LAP, Carvalhoc T, Rezended E, Aliahmada B, Papae JP, Kumara DK (2019) Exudate detection in fundus images using deeply-learnable features 104:62\u201369","DOI":"10.1016\/j.compbiomed.2018.10.031"},{"issue":"122742","key":"20703_CR25","first-page":"1","volume":"241","author":"QV Doa","year":"2024","unstructured":"Doa QV, Hoangb HT, Vub NV, Jesuse DAD, Breae LS, Nguyenb HX, Nguyend ATL, Leb TN, MyDinhb DT, Nguyenb MTB, Nguyenb HC, Van AT, VuLea H, Gillenf K, Vub TT, Luu HM (2024) Segmentation of hard exudate lesions in color fundus image using two-stage cnn-based methods. Expert Syst Appl 241(122742):1\u201318","journal-title":"Expert Syst Appl"},{"issue":"2017","key":"20703_CR26","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","volume":"420","author":"JH Tan","year":"2017","unstructured":"Tan JH, Fujita H, Sivaprasad S, Bhandary SV, Rao AK, Chua KC, Acharya UR (2017) Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf Sci 420(2017):66\u201376","journal-title":"Inf Sci"},{"key":"20703_CR27","doi-asserted-by":"crossref","unstructured":"Kar M,\u00a0Nath M,\u00a0Mishra M (2020) Retinal vessel segmentation and disc detection from color fundus images using inception module and residual connection. 3rd International Conference On Recent Trends In Advanced Computing 12","DOI":"10.1007\/978-981-16-6448-9_58"},{"key":"20703_CR28","unstructured":"Mallat S (2009) A Wavelet Tour to Signal Processing. Elsevier: Academic Press, Third\u00a0ed"},{"key":"20703_CR29","doi-asserted-by":"crossref","unstructured":"Daubechies (1992) Ten lectures on wavelets. SIAM","DOI":"10.1137\/1.9781611970104"},{"key":"20703_CR30","first-page":"194","volume":"4","author":"S Lahmiri","year":"2013","unstructured":"Lahmiri S (2013) Features extraction from high frequency domain for retina digital images classification. J Adv Inf Technol 4:194\u2013198","journal-title":"J Adv Inf Technol"},{"key":"20703_CR31","volume-title":"Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks","author":"M Kar","year":"2022","unstructured":"Kar M, Neog DR, Nath M (2022) Retinal vessel segmentation using multi-scale residual convolutional neural network (MSR-Net) combined with generative adversarial networks. Systems, and Signal Processing, Circuits"},{"key":"20703_CR32","unstructured":"Dosovitskiy A,\u00a0Beyer L,\u00a0Kolesnikov A,\u00a0Weissenborn D,\u00a0Zhai X,\u00a0Unterthiner T,\u00a0Dehghani M,\u00a0Minderer M,\u00a0Heigold G,\u00a0Gelly S,\u00a0Uszkoreit J,\u00a0Houlsby N (2020) An image is worth 16x16 words: Transformers for image recognition at scale. CoRR, vol arxiv:2010.11929"},{"key":"20703_CR33","unstructured":"Goodfellow IJ,\u00a0Pouget-Abadie J,\u00a0Mirza M,\u00a0Xu B,\u00a0Warde-Farley D,\u00a0Ozair S,\u00a0Courville A,\u00a0Bengio Y (2014) Generative adversarial networks"},{"key":"20703_CR34","doi-asserted-by":"crossref","unstructured":"Salehi SSM,\u00a0Erdogmus D,\u00a0Gholipour A (2017) Tversky loss function for image segmentation using 3d fully convolutional deep networks. Machine Learning in Medical Imaging 379\u2013387","DOI":"10.1007\/978-3-319-67389-9_44"},{"issue":"3","key":"20703_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal P, Pachade S, Kamble R, Kokare M, Deshmukh G, Sahasrabuddhe V, Meriaudeau F (2018) Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data 3(3):1\u20138","journal-title":"Data"},{"issue":"2","key":"20703_CR36","first-page":"196","volume":"34","author":"E Decenci\u00e8re","year":"2013","unstructured":"Decenci\u00e8re E, Cazugue G, Zhang X, Thibault G, Klein J-C, Meyer F, Marcotegui B, Quellec G, Lamard M, Danno R, Elie D, Massin P, Viktor Z, Erginay A, La\u00ff B, Chabouis A (2013) Teleophta: Machine learning and image processing methods for teleophthalmology. Innovation and Research in BioMedical engineering 34(2):196\u2013203","journal-title":"Innovation and Research in BioMedical engineering"},{"issue":"1","key":"20703_CR37","first-page":"1","volume":"16","author":"SK Vengalil","year":"2023","unstructured":"Vengalil SK, Krishnamurthy B, Sinha N (2023) Simultaneous segmentation of multiple structures in fundal images using multi-tasking deep neural networks. Frontiers Signal Process 16(1):1\u201313","journal-title":"Frontiers Signal Process"},{"issue":"2","key":"20703_CR38","first-page":"508","volume":"15","author":"DUN Qomariah","year":"2022","unstructured":"Qomariah DUN, Tjandrasa H, Fatichah C (2022) Exudate segmentation for diabetic retinopathy using modified fcn-8 and dice loss. Int J Intell Eng Syst 15(2):508\u2013520","journal-title":"Int J Intell Eng Syst"},{"key":"20703_CR39","doi-asserted-by":"crossref","unstructured":"Si Z,\u00a0Fu1 D,\u00a0Liu Y,\u00a0Huang Z (2021) Hard exudate segmentation in retinal image with attention mechanism. IET Image Processing 15:587\u2013597","DOI":"10.1049\/ipr2.12007"},{"key":"20703_CR40","doi-asserted-by":"crossref","unstructured":"Xue Y,\u00a0Xu T,\u00a0Zhang H, Long LR,\u00a0Huang X (2018) Segan: Adversarial network with multi-scale L1 loss for medical image segmentation. Neuroinformatics 383\u2013392","DOI":"10.1007\/s12021-018-9377-x"},{"issue":"1133575","key":"20703_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/1133575","volume":"2022","author":"SG Sandhya","year":"2022","unstructured":"Sandhya SG, Suhasini A, Hu Y-C (2022) Pixel-boundary-dependent segmentation method for early detection of diabetic retinopathy. Math Probl Eng 2022(1133575):1\u201312","journal-title":"Math Probl Eng"},{"key":"20703_CR42","unstructured":"Vision, Lab IP (2021) University of waterloo skin cancer database. https:\/\/uwaterloo.ca\/vision-image-processing-lab\/research-demos\/skin-cancer-detection. Accessed 12 Jan 2021"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20703-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20703-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20703-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T11:56:26Z","timestamp":1758974186000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20703-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,27]]},"references-count":42,"journal-issue":{"issue":"33","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["20703"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20703-7","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,27]]},"assertion":[{"value":"27 November 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical"}},{"value":"Authors declare no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}