{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:46:01Z","timestamp":1778168761189,"version":"3.51.4"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T00:00:00Z","timestamp":1646956800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10489-021-03043-5","type":"journal-article","created":{"date-parts":[[2022,3,11]],"date-time":"2022-03-11T11:05:04Z","timestamp":1646996704000},"page":"15105-15121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Hinge attention network: A joint model for diabetic retinopathy severity grading"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2432-7409","authenticated-orcid":false,"given":"Nagur Shareef","family":"Shaik","sequence":"first","affiliation":[]},{"given":"Teja Krishna","family":"Cherukuri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,3,11]]},"reference":[{"issue":"13","key":"3043_CR1","doi-asserted-by":"crossref","first-page":"5206","DOI":"10.1167\/iovs.16-19964","volume":"57","author":"MD Abr\u00e0moff","year":"2016","unstructured":"Abr\u00e0moff MD, Lou Y, Erginay A, Clarida W, Amelon R, Folk JC, Niemeijer M (2016) Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Investigative Ophthalmology & Visual Science 57(13):5206","journal-title":"Investigative Ophthalmology & Visual Science"},{"key":"3043_CR2","doi-asserted-by":"publisher","first-page":"54190","DOI":"10.1109\/ACCESS.2021.3070685","volume":"9","author":"MT Al-Antary","year":"2021","unstructured":"Al-Antary MT, Arafa Y (2021) Multi-scale attention network for diabetic retinopathy classification. IEEE Access 9:54190\u201354200","journal-title":"IEEE Access"},{"key":"3043_CR3","doi-asserted-by":"crossref","unstructured":"Amin J, Sharif M, Yasmin M (2016) A review on recent developments for detection of diabetic retinopathy. Scientifica","DOI":"10.1155\/2016\/6838976"},{"key":"3043_CR4","first-page":"54","volume":"75","author":"SV Bhandary","year":"2018","unstructured":"Bhandary SV, Rao KA (2018) Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Computers in Biology and Medicine 75:54\u201362","journal-title":"Computers in Biology and Medicine"},{"key":"3043_CR5","doi-asserted-by":"crossref","unstructured":"Bodapati JD, Shaik NS, Naralasetti V (2021) Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Humaniz Comput","DOI":"10.1007\/s12652-020-02727-z"},{"key":"3043_CR6","doi-asserted-by":"crossref","unstructured":"Bodapati JD, Shaik NS, Naralasetti V (2021) Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image Video Process:1\u20138","DOI":"10.1007\/s11760-020-01816-y"},{"key":"3043_CR7","doi-asserted-by":"crossref","unstructured":"Bodapati JD, Shaik NS, Naralasetti V, Mundukur NB (2020) Joint training of two-channel deep neural network for brain tumor classification. Signal Image Video Process:1\u20138","DOI":"10.1007\/s11760-020-01793-2"},{"key":"3043_CR8","doi-asserted-by":"crossref","unstructured":"Bodapati JD, Shareef SN, Naralasetti V, Mundukur NB (2021) Msenet: Multi-modal squeeze-and-excitation network for brain tumor severity prediction. Int J Pattern Recognit Artif Intell:2157005","DOI":"10.1142\/S0218001421570056"},{"issue":"6","key":"3043_CR9","doi-asserted-by":"publisher","first-page":"914","DOI":"10.3390\/electronics9060914","volume":"9","author":"JD Bodapati","year":"2020","unstructured":"Bodapati JD, Veeranjaneyulu N, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, Jo O (2020) Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics 9(6):914","journal-title":"Electronics"},{"key":"3043_CR10","doi-asserted-by":"crossref","unstructured":"Chen M, Shi X, Zhang Y, Wu D, Guizani M (2017) Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans Big Data:1\u20131","DOI":"10.1109\/TBDATA.2017.2777862"},{"key":"3043_CR11","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251\u20131258","DOI":"10.1109\/CVPR.2017.195"},{"key":"3043_CR12","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. Ieee, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"3","key":"3043_CR13","doi-asserted-by":"publisher","first-page":"307","DOI":"10.18280\/ria.340308","volume":"34","author":"V Dondeti","year":"2020","unstructured":"Dondeti V, Bodapati JD, Shareef SN, Naralasetti V (2020) Deep convolution features in non-linear embedding space for fundus image classification deep convolution features in non-linear embedding space for fundus image classification. Revue d\u2019Intelligence Artificielle 34(3):307\u2013313","journal-title":"Revue d\u2019Intelligence Artificielle"},{"issue":"21","key":"3043_CR14","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1056\/NEJM198605223142106","volume":"314","author":"GS Eisenbarth","year":"1986","unstructured":"Eisenbarth GS (1986) Type i diabetes mellitus. New England Journal of Medicine 314(21):1360\u20131368","journal-title":"New England Journal of Medicine"},{"key":"3043_CR15","doi-asserted-by":"crossref","unstructured":"Fukui H, Hirakawa T, Yamashita T, Fujiyoshi H (2019) Attention branch network: Learning of attention mechanism for visual explanation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2019.01096"},{"key":"3043_CR16","doi-asserted-by":"publisher","first-page":"3360","DOI":"10.1109\/ACCESS.2018.2888639","volume":"7","author":"Z Gao","year":"2019","unstructured":"Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J (2019) Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access 7:3360\u20133370","journal-title":"IEEE Access"},{"key":"3043_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.imu.2017.05.006","volume":"9","author":"M Habib","year":"2017","unstructured":"Habib M, Welikala R, Hoppe A, Owen C, Rudnicka A, Barman S (2017) Detection of microaneurysms in retinal images using an ensemble classifier. Informatics in Medicine Unlocked 9:44\u201357","journal-title":"Informatics in Medicine Unlocked"},{"key":"3043_CR18","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"3043_CR19","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"3043_CR20","unstructured":"International diabetes federation diabetes atlas (2019) https:\/\/www.diabetesatlas.org\/en\/. Accessed: 30-06-2021"},{"issue":"4","key":"3043_CR21","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1076\/opep.7.4.225.4171","volume":"7","author":"M Janghorbani","year":"2000","unstructured":"Janghorbani M, Jones RB, Allison SP (2000) Incidence of and risk factors for proliferative retinopathy and its association with blindness among diabetes clinic attenders. Ophthalmic Epidemiology 7(4):225\u2013241","journal-title":"Ophthalmic Epidemiology"},{"key":"3043_CR22","unstructured":"Kaggle (2019) Aptos 2019 blindness detection challenge. https:\/\/www.kaggle.com\/c\/aptos2019-blindnes-detection. Accessed: 2019-12-30"},{"issue":"6","key":"3043_CR23","doi-asserted-by":"publisher","first-page":"2021","DOI":"10.3390\/app10062021","volume":"10","author":"I Kandel","year":"2020","unstructured":"Kandel I, Castelli M (2020) Transfer learning with convolutional neural networks for diabetic retinopathy image classification. a review. Applied Sciences 10(6):2021","journal-title":"Applied Sciences"},{"key":"3043_CR24","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. In: 2019 IEEE International symposium on signal processing and information technology (ISSPIT). IEEE, pp 1\u20136","DOI":"10.1109\/ISSPIT47144.2019.9001846"},{"key":"3043_CR25","doi-asserted-by":"crossref","unstructured":"Kaur N, Chatterjee S, Acharyya M, Kaur J, Kapoor N, Gupta S (2016) A supervised approach for automated detection of hemorrhages in retinal fundus images. In: 2016 5th International conference on wireless networks and embedded systems (WECON). IEEE, pp 1\u20135","DOI":"10.1109\/WECON.2016.7993461"},{"issue":"5","key":"3043_CR26","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1109\/TMI.2019.2951844","volume":"39","author":"X Li","year":"2019","unstructured":"Li X, Hu X, Yu L, Zhu L, Fu C-W, Heng P-A (2019) Canet: Cross-disease attention network for joint diabetic retinopathy and diabetic macular edema grading. IEEE Transactions on Medical Imaging 39(5):1483\u20131493","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"3043_CR27","doi-asserted-by":"crossref","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","DOI":"10.1155\/2019\/3926930"},{"key":"3043_CR28","doi-asserted-by":"crossref","unstructured":"Luong M-T, Pham H, Manning CD (2015) Effective approaches to attention-based neural machine translation. In: International conference on learning representations","DOI":"10.18653\/v1\/D15-1166"},{"key":"3043_CR29","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.neucom.2020.04.148","volume":"452","author":"FJ Martinez-Murcia","year":"2021","unstructured":"Martinez-Murcia FJ, Ortiz A, Ram\u00edrez J, G\u00f3rriz JM, Cruz R (2021) Deep residual transfer learning for automatic diagnosis and grading of diabetic retinopathy. Neurocomputing 452:424\u2013434","journal-title":"Neurocomputing"},{"issue":"1","key":"3043_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/sym11010001","volume":"11","author":"M Mateen","year":"2019","unstructured":"Mateen M, Wen J, Song S, Huang Z et al (2019) Fundus image classification using vgg-19 architecture with pca and svd. Symmetry 11(1):1","journal-title":"Symmetry"},{"issue":"5","key":"3043_CR31","doi-asserted-by":"publisher","first-page":"711","DOI":"10.18280\/ts.370503","volume":"37","author":"M Mohammedhasan","year":"2020","unstructured":"Mohammedhasan M, U\u011fuz H (2020) A new early stage diabetic retinopathy diagnosis model using deep convolutional neural networks and principal component analysis. Traitement du Signal 37(5):711\u2013722","journal-title":"Traitement du Signal"},{"key":"3043_CR32","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","volume":"71","author":"L Nanni","year":"2017","unstructured":"Nanni L, Ghidoni S, Brahnam S (2017) Handcrafted vs. non-handcrafted features for computer vision classification. Pattern Recognition 71:158\u2013172","journal-title":"Pattern Recognition"},{"issue":"1","key":"3043_CR33","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1186\/s12938-019-0675-9","volume":"18","author":"E Noushin","year":"2019","unstructured":"Noushin E, Pourreza M, Masoudi K, Ghiasi Shirazi E (2019) Microaneurysm detection in fundus images using a two step convolution neural network. Biomed Eng Online 18(1):67","journal-title":"Biomed Eng Online"},{"key":"3043_CR34","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.artmed.2019.03.009","volume":"96","author":"R Pires","year":"2019","unstructured":"Pires R, Avila S, Wainer J, Valle E, Abramoff MD, Rocha A (2019) A data-driven approach to referable diabetic retinopathy detection. Artificial Intelligence in Medicine 96:93\u2013106","journal-title":"Artificial Intelligence in Medicine"},{"issue":"3","key":"3043_CR35","doi-asserted-by":"publisher","first-page":"25","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):25","journal-title":"Data"},{"key":"3043_CR36","doi-asserted-by":"publisher","first-page":"101561","DOI":"10.1016\/j.media.2019.101561","volume":"59","author":"P Porwal","year":"2020","unstructured":"Porwal P, Pachade S, Kokare M, Deshmukh G, Son J, Bae W, Liu L, Wang J, Liu X, Gao L et al (2020) Idrid: Diabetic retinopathy-segmentation and grading challenge. Medical Image Analysis 59:101561","journal-title":"Medical Image Analysis"},{"key":"3043_CR37","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1016\/j.cmpb.2016.09.018","volume":"137","author":"P Prenta\u0161i\u0107","year":"2016","unstructured":"Prenta\u0161i\u0107 P, Lon\u010dari\u0107 S (2016) Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Computer Methods and Programs in Biomedicine 137:281\u2013292","journal-title":"Computer Methods and Programs in Biomedicine"},{"key":"3043_CR38","doi-asserted-by":"crossref","unstructured":"Quellec G, Al Hajj H, Lamard M, Conze P-H, Massin P, Cochener B (2021) Explain: Explanatory artificial intelligence for diabetic retinopathy diagnosis. Med Image Anal:102118","DOI":"10.1016\/j.media.2021.102118"},{"issue":"6","key":"3043_CR39","doi-asserted-by":"publisher","first-page":"749","DOI":"10.3390\/sym11060749","volume":"11","author":"I Qureshi","year":"2019","unstructured":"Qureshi I, Ma J, Abbas Q (2019) Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry 11(6):749","journal-title":"Symmetry"},{"key":"3043_CR40","doi-asserted-by":"crossref","unstructured":"Rahim SS, Palade V, Holzinger A (2020) Image processing and machine learning techniques for diabetic retinopathy detection: A review. Artif Intell Mach Learn Digital Pathology:136\u2013154","DOI":"10.1007\/978-3-030-50402-1_9"},{"key":"3043_CR41","doi-asserted-by":"crossref","unstructured":"Razzak MI, Naz S, Zaib A (2018) Deep learning for medical image processing: Overview, challenges and the future. In: Classification in BioApps. Springer, pp 323\u2013350","DOI":"10.1007\/978-3-319-65981-7_12"},{"issue":"1","key":"3043_CR42","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1023\/B:VLSI.0000028532.53893.82","volume":"38","author":"AM Reza","year":"2004","unstructured":"Reza AM (2004) Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology 38(1):35\u201344","journal-title":"Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology"},{"issue":"6","key":"3043_CR43","doi-asserted-by":"publisher","first-page":"e0233514","DOI":"10.1371\/journal.pone.0233514","volume":"15","author":"M Shaban","year":"2020","unstructured":"Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Abu Khalifeh H, Ghazal M, Fraiwan L, Giridharan G, Sandhu H et al (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. Plos one 15(6):e0233514","journal-title":"Plos one"},{"issue":"6","key":"3043_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-021-01253-y","volume":"32","author":"NS Shaik","year":"2021","unstructured":"Shaik NS, Cherukuri TK (2021) Lesion-aware attention with neural support vector machine for retinopathy diagnosis. Machine Vision and Applications 32(6):1\u201313","journal-title":"Machine Vision and Applications"},{"key":"3043_CR45","doi-asserted-by":"crossref","unstructured":"Shaik NS, Cherukuri TK (2021) Multi-level attention network: application to brain tumor classification. Signal Image Video Process:1\u20138","DOI":"10.1007\/s11760-021-02022-0"},{"key":"3043_CR46","unstructured":"Shi X, Chen Z, Wang H, Yeung D-Y, Wong W-K, WOO, W-C (2015) Convolutional lstm network: A machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems. vol 28, Curran Associates, Inc., pp 802\u2013810"},{"issue":"4","key":"3043_CR47","doi-asserted-by":"publisher","first-page":"670","DOI":"10.3390\/sym13040670","volume":"13","author":"N Sikder","year":"2021","unstructured":"Sikder N, Masud M, Bairagi AK, Arif ASM, Nahid A-A, Alhumyani HA (2021) Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry 13(4):670","journal-title":"Symmetry"},{"key":"3043_CR48","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556"},{"key":"3043_CR49","unstructured":"Srivastava RK, Greff K, Schmidhuber J (2015) Training very deep networks. In: Advances in neural information processing systems. vol 28, Curran Associates, Inc., pp 2377\u20132385"},{"key":"3043_CR50","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"3043_CR51","doi-asserted-by":"crossref","unstructured":"Tsiknakis N, Theodoropoulos D, Manikis G, Ktistakis E, Boutsora O, Berto A, Scarpa F, Scarpa A, Fotiadis DI, Marias K (2021) Deep learning for diabetic retinopathy detection and classification based on fundus images: A review. Comput Biol Med:104599","DOI":"10.1016\/j.compbiomed.2021.104599"},{"key":"3043_CR52","first-page":"11","volume":"9","author":"L Van der Maaten","year":"2008","unstructured":"Van der Maaten L, Hinton G (2008) Visualizing data using t-sne. Journal of machine learning research 9:11","journal-title":"Journal of machine learning research"},{"key":"3043_CR53","unstructured":"Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, attend and tell: Neural image caption generation with visual attention. In: Proceedings of machine learning research (Lille, France, 09 2015), vol 37, PMLR, pp 2048\u20132057"},{"issue":"3","key":"3043_CR54","doi-asserted-by":"publisher","first-page":"556","DOI":"10.2337\/dc11-1909","volume":"35","author":"JW Yau","year":"2012","unstructured":"Yau JW, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen S-J, Dekker JM, Fletcher A, Grauslund J et al (2012) prevalence and major risk factors of diabetic retinopathy. Diabetes care 35(3):556\u2013564","journal-title":"Diabetes care"},{"issue":"5","key":"3043_CR55","doi-asserted-by":"publisher","first-page":"428","DOI":"10.4103\/0301-4738.100542","volume":"60","author":"Y Zheng","year":"2012","unstructured":"Zheng Y, He M, Congdon N (2012) The worldwide epidemic of diabetic retinopathy. Indian journal of ophthalmology 60(5):428","journal-title":"Indian journal of ophthalmology"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03043-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03043-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03043-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,20]],"date-time":"2024-09-20T03:11:56Z","timestamp":1726801916000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03043-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,11]]},"references-count":55,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["3043"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03043-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,11]]},"assertion":[{"value":"24 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}