{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:34:52Z","timestamp":1763642092871,"version":"3.45.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"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":["Prog Artif Intell"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s13748-025-00379-8","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T14:15:50Z","timestamp":1749564950000},"page":"549-561","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Implementation of a Modified U-Net Architecture for Hard Exudate Semantic Segmentation using Fundus Images"],"prefix":"10.1007","volume":"14","author":[{"given":"Amine","family":"El\u00a0hossi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8574-9584","authenticated-orcid":false,"given":"Abdelali","family":"Elmoufidi","sequence":"additional","affiliation":[]},{"given":"Mourad","family":"Nachaoui","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"issue":"3","key":"379_CR1","first-page":"1","volume":"33","author":"SSR Dhanushkodi","year":"2013","unstructured":"Dhanushkodi, S.S.R., VASUKI, M.: Diagnosis system for diabetic retinopathy to prevent vision loss. Appl. Med. Inform. 33(3), 1\u201311 (2013)","journal-title":"Appl. Med. Inform."},{"issue":"2","key":"379_CR2","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1007\/s11764-019-00753-1","volume":"13","author":"GO Antwi","year":"2019","unstructured":"Antwi, G.O., et al.: Associations between e-cigarette and combustible cigarette use among US cancer survivors: implications for research and practice. J. Cancer Surviv. 13(2), 316\u2013325 (2019)","journal-title":"J. Cancer Surviv."},{"issue":"1","key":"379_CR3","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1186\/s13098-023-01202-x","volume":"15","author":"ET Fenta","year":"2023","unstructured":"Fenta, E.T., et al.: Prevalence and predictors of chronic kidney disease among type 2 diabetic patients worldwide, systematic review and meta-analysis. Diabetol. & Metab. Syndr. 15(1), 245 (2023)","journal-title":"Diabetol. & Metab. Syndr."},{"issue":"2","key":"379_CR4","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.diabres.2013.11.002","volume":"103","author":"L Guariguata","year":"2014","unstructured":"Guariguata, L., et al.: Global estimates of diabetes prevalence for 2013 and projections for 2035. Diabetes Res. Clin. Pract. 103(2), 137\u2013149 (2014)","journal-title":"Diabetes Res. Clin. Pract."},{"key":"379_CR5","first-page":"785","volume":"10574","author":"P Chudzik","year":"2018","unstructured":"Chudzik, P., et al.: Exudate segmentation using fully convolutional neural networks and inception modules. Med. Imaging 2018: Image Processing. 10574, 785\u2013792 (2018). (SPIE)","journal-title":"Med. Imaging 2018: Image Processing."},{"issue":"1","key":"379_CR6","doi-asserted-by":"publisher","first-page":"80","DOI":"10.7326\/0003-4819-108-1-80","volume":"108","author":"P Szolovits","year":"1988","unstructured":"Szolovits, P., Patil, R.S., Schwartz, W.B.: Artificial intelligence in medical diagnosis. Ann. Intern. Med. 108(1), 80\u201387 (1988)","journal-title":"Ann. Intern. Med."},{"issue":"5","key":"379_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10462-024-10770-x","volume":"57","author":"H Naz","year":"2024","unstructured":"Naz, H., Ahuja, N.J., Nijhawan, R.: Diabetic retinopathy detection using supervised and unsupervised deep learning: a review study. Artif. Intell. Rev. 57(5), 1\u201366 (2024)","journal-title":"Artif. Intell. Rev."},{"issue":"27","key":"379_CR8","doi-asserted-by":"publisher","first-page":"41701","DOI":"10.1007\/s11042-023-15110-9","volume":"82","author":"A Skouta","year":"2023","unstructured":"Skouta, A., et al.: Deep learning for diabetic retinopathy assessments: a literature review. multimed. Tools and Appl. 82(27), 41701\u201341766 (2023)","journal-title":"multimed. Tools and Appl."},{"key":"379_CR9","doi-asserted-by":"crossref","unstructured":"El Hossi, A., et al.: \u201cApplied CNN for automatic diabetic retinopathy assessment using fundus images\u201d. In: Business intelligence: 6th international conference, CBI 2021, beni mellal, morocco, may 27\u201329, 2021, proceedings. Springer. 425\u2013433 (2021)","DOI":"10.1007\/978-3-030-76508-8_31"},{"key":"379_CR10","unstructured":"O\u2019Shea, K., Nash, R.: \u201cAn introduction to convolutional neural networks\u201d. In: arXiv preprint arXiv:1511.08458 (2015)"},{"issue":"3","key":"379_CR11","first-page":"585","volume":"37","author":"SH Talib","year":"2023","unstructured":"Talib, S.H., Al-Thahab, O.Q.J.: Automated retinal hard exudate detection using novel rhombus multilevel segmentation algorithm. Rev. d\u2019Intell. Artif. 37(3), 585 (2023)","journal-title":"Rev. d\u2019Intell. Artif."},{"key":"379_CR12","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1016\/j.cmpb.2018.02.011","volume":"158","author":"W Kusakunniran","year":"2018","unstructured":"Kusakunniran, W., et al.: Hard exudates segmentation based on learned initial seeds and iterative graph cut. Comput. Methods Programs Biomed. 158, 173\u2013183 (2018)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"3","key":"379_CR13","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1049\/ipr2.12007","volume":"15","author":"Z Si","year":"2021","unstructured":"Si, Z., et al.: Hard exudate segmentation in retinal image with attention mechanism. IET Image Proc. 15(3), 587\u2013597 (2021)","journal-title":"IET Image Proc."},{"issue":"3","key":"379_CR14","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1109\/JBHI.2021.3108169","volume":"26","author":"Q Liu","year":"2021","unstructured":"Liu, Q., et al.: Dual-branch network with dual-sampling modulated dice loss for hard exudate segmentation in color fundus images. IEEE J. Biomed. Health Inform. 26(3), 1091\u20131102 (2021)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"379_CR15","doi-asserted-by":"publisher","first-page":"167225","DOI":"10.1109\/ACCESS.2020.3023273","volume":"8","author":"Y Zong","year":"2020","unstructured":"Zong, Y., et al.: U-net based method for automatic hard exudates segmentation in fundus images using inception module and residual connection. IEEE Access 8, 167225\u2013167235 (2020)","journal-title":"IEEE Access"},{"issue":"1","key":"379_CR16","volume":"1108","author":"N Nur","year":"2018","unstructured":"Nur, N., Tjandrasa, H.: Exudate segmentation in retinal images of diabetic retinopathy using saliency method based on region. J. Phys: Conf. Ser. 1108(1), 012110 (2018). (IOP Publishing)","journal-title":"J. Phys: Conf. Ser."},{"key":"379_CR17","doi-asserted-by":"crossref","unstructured":"Guo, S., et al.: A lightweight neural network for hard exudate segmentation of fundus image, Artificial Neural Networks and Machine Learning\u2013ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17\u201319, 2019, Proceedings, Part III 28, 189\u2013199, Springer (2019)","DOI":"10.1007\/978-3-030-30508-6_16"},{"key":"379_CR18","doi-asserted-by":"publisher","first-page":"185514","DOI":"10.1109\/ACCESS.2020.3029117","volume":"8","author":"K Caixia","year":"2020","unstructured":"Caixia, K., et al.: An enhanced residual U-Net for microaneurysms and exudates segmentation in fundus images. IEEE Access 8, 185514\u2013185525 (2020). (IEEE)","journal-title":"IEEE Access"},{"issue":"4","key":"379_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42600-023-00305-8","volume":"39","author":"A Elmoufidi","year":"2023","unstructured":"Elmoufidi, A., Hossi, A.E.L., Nachaoui, M.: Machine learning for glaucoma detection using fundus images. Res. on Biomed. Engineering 39(4), 1\u201313 (2023)","journal-title":"Res. on Biomed. Engineering"},{"key":"379_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2022.3177141","volume":"71","author":"A Elmoufidi","year":"2022","unstructured":"Elmoufidi, A.: Deep multiple instance learning for automatic breast cancer assessment using digital mammography. IEEE Trans. Instrum. Meas. 71, 1\u201313 (2022)","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"01","key":"379_CR21","doi-asserted-by":"publisher","first-page":"2350012","DOI":"10.1142\/S0219467823500122","volume":"23","author":"A Elmoufidi","year":"2023","unstructured":"Elmoufidi, A., et al.: CNN with multiple inputs for automatic glaucoma assessment using fundus images. Int. J. of Image and Graphics 23(01), 2350012 (2023)","journal-title":"Int. J. of Image and Graphics"},{"issue":"1","key":"379_CR22","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1007\/s42979-022-01482-6","volume":"4","author":"A Elmoufidi","year":"2022","unstructured":"Elmoufidi, A., Ammoun, H.: Diabetic retinopathy prevention using efficientNetB3 architecture and fundus photography. SN Comput. Sci. 4(1), 78 (2022)","journal-title":"SN Comput. Sci."},{"issue":"3","key":"379_CR23","first-page":"359","volume":"14","author":"DUN Qomariah","year":"2021","unstructured":"Qomariah, D.U.N., Tjandrasa, H., Fatichah, C.: Segmentation of microaneurysms for early detection of diabetic retinopathy using MResUNet. International J. of Intell. Engineering and Syst. 14(3), 359\u2013373 (2021)","journal-title":"International J. of Intell. Engineering and Syst."},{"key":"379_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Q., et al.: \u201cAOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images\u201d. In: arXiv preprint arXiv:2106.02800 (2021)","DOI":"10.1167\/tvst.11.8.7"},{"issue":"1","key":"379_CR25","doi-asserted-by":"publisher","first-page":"012057","DOI":"10.1088\/1757-899X\/1099\/1\/012057","volume":"1099","author":"R Deepa","year":"2021","unstructured":"Deepa, R., Narayanan, N.K.: Retinal microaneurysm detection by CNN. IOP Conf. Ser.: Mater. Sci. and Engineering. 1099(1), 012057 (2021). (IOP Publishing)","journal-title":"IOP Conf. Ser.: Mater. Sci. and Engineering."},{"key":"379_CR26","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.patrec.2020.02.026","volume":"133","author":"K Shankar","year":"2020","unstructured":"Shankar, K., et al.: Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recogn. Lett. 133, 210\u2013216 (2020)","journal-title":"Pattern Recogn. Lett."},{"key":"379_CR27","doi-asserted-by":"crossref","unstructured":"Kassani, S. H., et al.: \u201cDiabetic retinopathy classification using a modified xception architecture\u201d. In: 2019 IEEE international symposium on signal processing and information technology (ISSPIT). IEEE, 1\u20136 (2019)","DOI":"10.1109\/ISSPIT47144.2019.9001846"},{"issue":"3","key":"379_CR28","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1007\/s00521-018-03974-0","volume":"32","author":"D Jude Hemanth","year":"2020","unstructured":"Jude Hemanth, D., Deperlioglu, O., Kose, U.: An enhanced diabetic retinopathy detection and classification approach using deep convolutional neural network. Neural Comput. Appl. 32(3), 707\u2013721 (2020)","journal-title":"Neural Comput. Appl."},{"key":"379_CR29","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: \u201cLesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks\u201d. In: International conference on medical image computing and computer-assisted intervention. Springer, 533\u2013540 (2017)","DOI":"10.1007\/978-3-319-66179-7_61"},{"key":"379_CR30","doi-asserted-by":"crossref","unstructured":"ElMOUFIDI, A., Amoun, H.: \u201cEfficientNetB3 Architecture for Diabetic Retinopathy Assessment using Fundus Images\u201d. In: (2021)","DOI":"10.21203\/rs.3.rs-609899\/v1"},{"issue":"3","key":"379_CR31","doi-asserted-by":"publisher","first-page":"25","DOI":"10.21227\/H25W98","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal, P., et al.: Indian diabetic retinopathy image dataset (IDRiD). Data 3(3), 25 (2018). https:\/\/doi.org\/10.21227\/H25W98","journal-title":"Data"},{"issue":"3","key":"379_CR32","doi-asserted-by":"publisher","first-page":"25","DOI":"10.3390\/data3030025","volume":"3","author":"P Porwal","year":"2018","unstructured":"Porwal, P., et al.: Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research. Data 3(3), 25 (2018)","journal-title":"Data"},{"key":"379_CR33","doi-asserted-by":"crossref","unstructured":"Huang, Y., et al.: \u201cAutomated hemorrhage detection from coarsely annotated fundus images in diabetic retinopathy\u201d. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE, 1369\u20131372 (2020)","DOI":"10.1109\/ISBI45749.2020.9098319"},{"key":"379_CR34","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"S Sahu","year":"2019","unstructured":"Sahu, S., et al.: An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt. & Laser Technology 110, 87\u201398 (2019)","journal-title":"Opt. & Laser Technology"},{"key":"379_CR35","doi-asserted-by":"crossref","unstructured":"Li, L., Si, Y., Jia, Z.: Medical image enhancement based on CLAHE and unsharp masking in NSCT domain. J. of Med. Imaging and Health Inform. 8(3), 431\u2013438 (2018)","DOI":"10.1166\/jmihi.2018.2328"},{"key":"379_CR36","doi-asserted-by":"crossref","unstructured":"Setiawan, A. W., et al.: \u201cColor retinal image enhancement using CLAHE\u201d. In: International conference on ICT for smart society. IEEE, 1\u20133 (2013)","DOI":"10.1109\/ICTSS.2013.6588092"},{"key":"379_CR37","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: \u201cU-net: Convolutional networks for biomedical image segmentation\u201d. In: International Conference on Medical image computing and computer-assisted intervention. Springer. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"379_CR38","doi-asserted-by":"publisher","first-page":"70853","DOI":"10.1109\/ACCESS.2023.3294443","volume":"11","author":"B Naveen Kumar","year":"2023","unstructured":"Naveen Kumar, B., et al.: Redefining Retinal Lesion Segmentation: A Quantum Leap With DL-UNet Enhanced Auto Encoder-Decoder for Fundus Image Analysis. IEEE Access 11, 70853\u201370864 (2023)","journal-title":"IEEE Access"},{"key":"379_CR39","first-page":"17","volume":"10953","author":"F Zabihollahy","year":"2019","unstructured":"Zabihollahy, F., Lochbihler, A., Ukwatta, E.: \u201cDeep learning based approach for fully automated detection and segmentation of hard exudate from retinal images\u2019\u2019. Medical Imaging 2019: Biomedical Applications in Molecular. Structural, and Functional Imaging. 10953, 17\u201322 (2019). (SPIE)","journal-title":"Structural, and Functional Imaging."},{"key":"379_CR40","doi-asserted-by":"crossref","unstructured":"Qomariah, D. U. N., Tjandrasa, H., Fatichah, C.: \u201cExudate Segmentation for Diabetic Retinopathy Using Modified FCN-8 and Dice Loss.\u201d In: International Journal of Intelligent Engineering & Systems15(2), (2022)","DOI":"10.22266\/ijies2022.0430.45"},{"key":"379_CR41","doi-asserted-by":"publisher","first-page":"126486","DOI":"10.1109\/ACCESS.2024.3455433","volume":"12","author":"M Yinghua","year":"2024","unstructured":"Yinghua, M., et al.: Hard Exudates Segmentation in Diabetic Retinopathy Using DiaRetDB1. IEEE Access 12, 126486\u2013126502 (2024). https:\/\/doi.org\/10.1109\/ACCESS.2024.3455433","journal-title":"IEEE Access"},{"key":"379_CR42","doi-asserted-by":"crossref","unstructured":"Wang, B., et al.: CSU-Net: A context spatial U-Net for accurate blood vessel segmentation in fundus images. IEEE J. Biomed. Health Inform. 25(4), 1128\u20131138 (2020)","DOI":"10.1109\/JBHI.2020.3011178"}],"container-title":["Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-025-00379-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13748-025-00379-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13748-025-00379-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:21:46Z","timestamp":1763641306000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13748-025-00379-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":42,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["379"],"URL":"https:\/\/doi.org\/10.1007\/s13748-025-00379-8","relation":{},"ISSN":["2192-6352","2192-6360"],"issn-type":[{"type":"print","value":"2192-6352"},{"type":"electronic","value":"2192-6360"}],"subject":[],"published":{"date-parts":[[2025,6,10]]},"assertion":[{"value":"1 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 June 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}