{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,12]],"date-time":"2026-04-12T03:09:53Z","timestamp":1775963393208,"version":"3.50.1"},"reference-count":62,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T00:00:00Z","timestamp":1655769600000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1007\/s00521-022-07471-3","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T19:39:42Z","timestamp":1655840382000},"page":"18663-18683","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["Deep learning enabled optimized feature selection and classification for grading diabetic retinopathy severity in the fundus image"],"prefix":"10.1007","volume":"34","author":[{"given":"A. Mary","family":"Dayana","sequence":"first","affiliation":[]},{"given":"W. R. Sam","family":"Emmanuel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,6,21]]},"reference":[{"issue":"3","key":"7471_CR1","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1109\/TMI.2020.3037771","volume":"40","author":"Y Zhou","year":"2021","unstructured":"Zhou Y, Wang B, Huang L et al (2021) A benchmark for studying diabetic retinopathy: segmentation, grading, and transferability. IEEE Trans Med Imaging 40(3):818\u2013828","journal-title":"IEEE Trans Med Imaging"},{"key":"7471_CR2","unstructured":"IDF Diabetes Atlas|Tenth Edition. https:\/\/diabetesatlas.org\/. Accessed 13 Dec 2021"},{"issue":"3","key":"7471_CR3","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1109\/TBME.2017.2707578","volume":"65","author":"SS Kar","year":"2018","unstructured":"Kar SS, Maity SP (2018) Automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Trans Biomed Eng 65(3):608\u2013618","journal-title":"IEEE Trans Biomed Eng"},{"issue":"5","key":"7471_CR4","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1109\/TMI.2018.2794988","volume":"37","author":"L Dai","year":"2018","unstructured":"Dai L, Fang R, Li H et al (2018) Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans Med Imaging 37(5):1149\u20131161","journal-title":"IEEE Trans Med Imaging"},{"issue":"10","key":"7471_CR5","doi-asserted-by":"publisher","first-page":"3709","DOI":"10.1109\/JBHI.2021.3052916","volume":"25","author":"L Ju","year":"2021","unstructured":"Ju L, Wang X, Zhao X et al (2021) Synergic adversarial label learning for grading retinal diseases via knowledge distillation and multi-task learning. IEEE J Biomed Heal Informat 25(10):3709\u20133720","journal-title":"IEEE J Biomed Heal Informat"},{"issue":"6","key":"7471_CR6","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1109\/TMI.2018.2885376","volume":"38","author":"R Wang","year":"2019","unstructured":"Wang R, Chen B, Meng D, Wang L (2019) Weakly supervised lesion detection from fundus images. IEEE Trans Med Imaging 38(6):1501\u20131512","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"7471_CR7","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":"7471_CR8","doi-asserted-by":"publisher","first-page":"30439","DOI":"10.1007\/s11042-020-09288-5","volume":"79","author":"J Vaishnavi","year":"2020","unstructured":"Vaishnavi J, Ravi S, Anbarasi A (2020) An efficient adaptive histogram based segmentation and extraction model for the classification of severities on diabetic retinopathy. Multimed Tools Appl 79:30439\u201330452","journal-title":"Multimed Tools Appl"},{"issue":"10","key":"7471_CR9","doi-asserted-by":"publisher","first-page":"9825","DOI":"10.1007\/s12652-020-02727-z","volume":"12","author":"JD Bodapati","year":"2021","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 12(10):9825\u20139839","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"April","key":"7471_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102600","volume":"68","author":"S Das","year":"2021","unstructured":"Das S, Kharbanda K, Suchetha M et al (2021) Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed Signal Process Control 68(April):102600","journal-title":"Biomed Signal Process Control"},{"key":"7471_CR11","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, Sait ARW, Gupta D et al (2020) Automated detection and classification of fundus diabetic retinopathy images using synergic deep learning model. Pattern Recognit Lett 133:210\u2013216","journal-title":"Pattern Recognit Lett"},{"key":"7471_CR12","doi-asserted-by":"publisher","first-page":"816","DOI":"10.3390\/e23070816","volume":"23","author":"P Liu","year":"2021","unstructured":"Liu P, Yang X, Jin B, Zhou Q (2021) Diabetic retinal grading using attention-based bilinear convolutional neural network and complement cross entropy. Entropy 23:816","journal-title":"Entropy"},{"key":"7471_CR13","doi-asserted-by":"publisher","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":"7471_CR14","first-page":"254","volume-title":"Communications in computer and information science-CCIS 2020","author":"A Pradhan","year":"2020","unstructured":"Pradhan A, Sarma B, Nath RK et al (2020) Diabetic retinopathy detection on retinal fundus images using convolutional neural network. In: Bhattacharjee A, Borgohain SK, Soni B, Verma G, Gao X-Z (eds) Communications in computer and information science-CCIS 2020. Springer, Singapore, pp 254\u2013266"},{"key":"7471_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00318-9","author":"G Kalyani","year":"2021","unstructured":"Kalyani G, Janakiramaiah B, Karuna A, Prasad LVN (2021) Diabetic retinopathy detection and classification using capsule networks. Complex Intell Syst. https:\/\/doi.org\/10.1007\/s40747-021-00318-9","journal-title":"Complex Intell Syst"},{"issue":"3","key":"7471_CR16","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 Appl 32(3):707\u2013721","journal-title":"Neural Comput Appl"},{"issue":"1","key":"7471_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00500-016-2442-1","volume":"22","author":"I Aljarah","year":"2018","unstructured":"Aljarah I, Faris H, Mirjalili S (2018) Optimizing connection weights in neural networks using the whale optimization algorithm. Soft Comput 22(1):1\u201315","journal-title":"Soft Comput"},{"issue":"3","key":"7471_CR18","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1016\/j.asej.2020.01.007","volume":"11","author":"AM Hemeida","year":"2020","unstructured":"Hemeida AM, Hassan SA, Mohamed AAA et al (2020) Nature-inspired algorithms for feed-forward neural network classifiers: a survey of one decade of research. Ain Shams Eng J 11(3):659\u2013675","journal-title":"Ain Shams Eng J"},{"issue":"2","key":"7471_CR19","doi-asserted-by":"publisher","first-page":"2649","DOI":"10.1007\/s12652-020-02426-9","volume":"12","author":"C Bhardwaj","year":"2021","unstructured":"Bhardwaj C, Jain S, Sood M (2021) Hierarchical severity grade classification of non-proliferative diabetic retinopathy. J Ambient Intell Humaniz Comput 12(2):2649\u20132670","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"7471_CR20","doi-asserted-by":"publisher","first-page":"15939","DOI":"10.1109\/ACCESS.2021.3052870","volume":"9","author":"E Abdelmaksoud","year":"2021","unstructured":"Abdelmaksoud E, El-Sappagh S, Barakat S et al (2021) Automatic diabetic retinopathy grading system based on detecting multiple retinal lesions. IEEE Access 9:15939\u201315960","journal-title":"IEEE Access"},{"key":"7471_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.7717\/peerj-cs.456","volume":"7","author":"LK Ramasamy","year":"2021","unstructured":"Ramasamy LK, Padinjappurathu SG, Kadry S, Dama\u0161evi\u010dius R (2021) Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier. PeerJ Comput Sci 7:1\u201321","journal-title":"PeerJ Comput Sci"},{"issue":"11","key":"7471_CR22","doi-asserted-by":"publisher","first-page":"1959","DOI":"10.1007\/s11517-017-1638-6","volume":"55","author":"Q Abbas","year":"2017","unstructured":"Abbas Q, Fondon I, Sarmiento A et al (2017) Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput 55(11):1959\u20131974","journal-title":"Med Biol Eng Comput"},{"key":"7471_CR23","doi-asserted-by":"publisher","first-page":"3704","DOI":"10.3390\/s21113704","volume":"21","author":"WL Alyoubi","year":"2021","unstructured":"Alyoubi WL, Abulkhair MF, Shalash WM (2021) DR fundus image classification and lesion localization system using deep learning. Sensors 21:3704","journal-title":"Sensors"},{"key":"7471_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3062638","author":"Y Yang","year":"2021","unstructured":"Yang Y, Shang F, Wu B et al (2021) Robust collaborative learning of patch-level and image-level annotations for diabetic retinopathy grading from fundus image. IEEE Trans Cybern. https:\/\/doi.org\/10.1109\/TCYB.2021.3062638","journal-title":"IEEE Trans Cybern"},{"issue":"2","key":"7471_CR25","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1002\/ima.22482","volume":"31","author":"AB Kadan","year":"2020","unstructured":"Kadan AB, Subbian PS (2020) Optimized hybrid classifier for diagnosing diabetic retinopathy: iterative blood vessel segmentation process. Int J Imaging Syst Technol 31(2):1009\u20131033","journal-title":"Int J Imaging Syst Technol"},{"key":"7471_CR26","doi-asserted-by":"publisher","first-page":"1173","DOI":"10.1002\/ima.22419","volume":"30","author":"TV Roshini","year":"2020","unstructured":"Roshini TV, Ravi RV, Reema Mathew A et al (2020) Automatic diagnosis of diabetic retinopathy with the aid of adaptive average filtering with optimized deep convolutional neural network. Int J Imaging Syst Technol 30:1173\u20131193","journal-title":"Int J Imaging Syst Technol"},{"key":"7471_CR27","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1007\/s12065-020-00400-0","volume":"14","author":"AS Jadhav","year":"2020","unstructured":"Jadhav AS, Patil PB, Biradar S (2020) Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning. Evol Intell 14:1431\u20131448","journal-title":"Evol Intell"},{"key":"7471_CR28","first-page":"659","volume":"45","author":"QM Zhang","year":"2021","unstructured":"Zhang QM, Luo J, Cengiz K (2021) An optimized deep learning based technique for grading and extraction of diabetic retinopathy severities. Inform 45:659\u2013665","journal-title":"Inform"},{"key":"7471_CR29","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1166\/jmihi.2021.3362","volume":"11","author":"J Jayanthi","year":"2020","unstructured":"Jayanthi J, Jayasankar T, Krishnaraj N et al (2020) An intelligent particle swarm optimization with convolutional neural network for diabetic retinopathy classification model. J Med Imaging Heal Inform 11:803\u2013809","journal-title":"J Med Imaging Heal Inform"},{"issue":"3","key":"7471_CR30","first-page":"2815","volume":"66","author":"PT Nguyen","year":"2021","unstructured":"Nguyen PT, Bich Huynh VD, Vo KD et al (2021) An optimal deep learning based computer-aided diagnosis system for diabetic retinopathy. Comput Mater Contin 66(3):2815\u20132830","journal-title":"Comput Mater Contin"},{"key":"7471_CR31","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1049\/ipr2.12047","volume":"15","author":"B Keerthiveena","year":"2021","unstructured":"Keerthiveena B, Esakkirajan S, Subudhi BN, Veerakumar T (2021) A hybrid BPSO-SVM for feature selection and classification of ocular health. IET Image Process 15:542\u2013555","journal-title":"IET Image Process"},{"key":"7471_CR32","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1504\/IJNBM.2019.104935","volume":"8","author":"SN Randive","year":"2019","unstructured":"Randive SN, Senapati RK, Rahulkar AD (2019) A self adaptive optimization for diabetic retinopathy detection with neural classification. Int J Nano Biomater 8:204\u2013227","journal-title":"Int J Nano Biomater"},{"issue":"3","key":"7471_CR33","doi-asserted-by":"publisher","DOI":"10.1002\/CNM.3560","volume":"38","author":"R Pugal Priya","year":"2022","unstructured":"Pugal Priya R, Saradadevi Sivarani T, Gnana Saravanan A (2022) Deep long and short term memory based Red Fox optimization algorithm for diabetic retinopathy detection and classification. Int J Numer Method Biomed Eng 38(3):e3560. https:\/\/doi.org\/10.1002\/CNM.3560","journal-title":"Int J Numer Method Biomed Eng"},{"key":"7471_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S11063-021-10734-0","volume":"24","author":"VD Vinayaki","year":"2022","unstructured":"Vinayaki VD, Kalaiselvi R (2022) Multithreshold image segmentation technique using remora optimization algorithm for diabetic retinopathy detection from fundus images. Neural Process Lett 24:1\u201322. https:\/\/doi.org\/10.1007\/S11063-021-10734-0","journal-title":"Neural Process Lett"},{"key":"7471_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/S11042-022-12492-0","volume":"2022","author":"AM Dayana","year":"2022","unstructured":"Dayana AM, Emmanuel WRS (2022) An enhanced swarm optimization-based deep neural network for diabetic retinopathy classification in fundus images. Multimed Tools Appl 2022:1\u201332. https:\/\/doi.org\/10.1007\/S11042-022-12492-0","journal-title":"Multimed Tools Appl"},{"key":"7471_CR36","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 et al (2021) Severity classification of diabetic retinopathy using an ensemble learning algorithm through analyzing retinal images. Symmetry (Basel) 13:670","journal-title":"Symmetry (Basel)"},{"key":"7471_CR37","doi-asserted-by":"publisher","first-page":"2434","DOI":"10.1109\/TMI.2019.2906319","volume":"38","author":"C Playout","year":"2019","unstructured":"Playout C, Duval R, Cheriet F (2019) A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans Med Imaging 38:2434\u20132444. https:\/\/doi.org\/10.1109\/TMI.2019.2906319","journal-title":"IEEE Trans Med Imaging"},{"key":"7471_CR38","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1016\/j.net.2018.12.013","volume":"51","author":"CR Park","year":"2019","unstructured":"Park CR, Lee Y (2019) Fast non-local means noise reduction algorithm with acceleration function for improvement of image quality in gamma camera system: a phantom study. Nucl Eng Technol 51:719\u2013722","journal-title":"Nucl Eng Technol"},{"key":"7471_CR39","doi-asserted-by":"publisher","first-page":"01003","DOI":"10.1051\/itmconf\/20192901003","volume":"29","author":"M Judson","year":"2019","unstructured":"Judson M, Viger T, Lim H (2019) Efficient and robust non-local means denoising methods for biomedical images. ITM Web Conf 29:01003","journal-title":"ITM Web Conf"},{"key":"7471_CR40","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.optlastec.2018.06.061","volume":"110","author":"SS Sonali","year":"2019","unstructured":"Sonali SS, Singh AK et al (2019) An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE. Opt Laser Technol 110:87\u201398","journal-title":"Opt Laser Technol"},{"issue":"3","key":"7471_CR41","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1016\/j.bbe.2018.05.006","volume":"38","author":"J Kaur","year":"2018","unstructured":"Kaur J, Mittal D (2018) Estimation of severity level of non-proliferative diabetic retinopathy for clinical aid. Biocybern Biomed Eng 38(3):708\u2013732","journal-title":"Biocybern Biomed Eng"},{"key":"7471_CR42","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0262-8856(98)00102-4","volume":"17","author":"J Weickert","year":"1999","unstructured":"Weickert J (1999) Coherence-enhancing diffusion of colour images. Image Vis Comput 17:201\u2013212","journal-title":"Image Vis Comput"},{"key":"7471_CR43","doi-asserted-by":"publisher","first-page":"73752E","DOI":"10.1117\/12.839214","volume":"7375","author":"H Wang","year":"2008","unstructured":"Wang H, Qian K, Gao W et al (2008) Partial-differential-equation-based coherence-enhancing de-noising for fringe patterns. ICEM 2008 Int Conf Exp Mech 7375:73752E","journal-title":"ICEM 2008 Int Conf Exp Mech"},{"issue":"12","key":"7471_CR44","doi-asserted-by":"publisher","first-page":"2393","DOI":"10.3390\/app8122393","volume":"8","author":"L Sun","year":"2018","unstructured":"Sun L, Meng X, Xu J, Zhang S (2018) An image segmentation method based on improved regularized level set model. Appl Sci 8(12):2393","journal-title":"Appl Sci"},{"key":"7471_CR45","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1049\/joe.2018.5345","volume":"2","author":"A Abdullah Yahya","year":"2019","unstructured":"Abdullah Yahya A, Tan J, Su B et al (2019) Image edge detection method based on anisotropic diffusion and total variation models. J Eng 2:455\u2013460","journal-title":"J Eng"},{"issue":"4","key":"7471_CR46","doi-asserted-by":"publisher","first-page":"1613","DOI":"10.1109\/TIP.2018.2880568","volume":"28","author":"Y He","year":"2019","unstructured":"He Y, Ni LM (2019) A novel scheme based on the diffusion to edge detection. IEEE Trans Image Process 28(4):1613\u20131624","journal-title":"IEEE Trans Image Process"},{"key":"7471_CR47","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","volume":"501","author":"T Li","year":"2019","unstructured":"Li T, Gao Y, Wang K et al (2019) Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Inf Sci (Ny) 501:511\u2013522","journal-title":"Inf Sci (Ny)"},{"key":"7471_CR48","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/6644071","author":"Y Xu","year":"2021","unstructured":"Xu Y, Zhou Z, Li X et al (2021) FFU-net: feature fusion U-net for lesion segmentation of diabetic retinopathy. Biomed Res Int. https:\/\/doi.org\/10.1155\/2021\/6644071","journal-title":"Biomed Res Int"},{"key":"7471_CR49","unstructured":"Xu B, Wang N, Chen T, Li M (2015) Empirical evaluation of rectified activations in convolutional network"},{"key":"7471_CR50","first-page":"401","volume-title":"Communications in computer and information science, CCIS-1440","author":"M Dayana","year":"2021","unstructured":"Dayana M, Emmanuel S (2021) Attention-based deep fusion network for retinal lesion segmentation in fundus image. In: Singh M et. al (eds) Communications in computer and information science, CCIS-1440. Springer, pp 401\u2013409"},{"issue":"5","key":"7471_CR51","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1109\/LSP.2018.2817176","volume":"25","author":"T Chakraborti","year":"2018","unstructured":"Chakraborti T, McCane B, Mills S, Pal U (2018) LOOP descriptor: local optimal-oriented pattern. IEEE Signal Process Lett 25(5):635\u2013639","journal-title":"IEEE Signal Process Lett"},{"key":"7471_CR52","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H et al (2019) Harris hawks optimization: algorithm and applications. Future Gener Comput Syst 97:849\u2013872","journal-title":"Future Gener Comput Syst"},{"key":"7471_CR53","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.neucom.2017.04.060","volume":"267","author":"M Wang","year":"2017","unstructured":"Wang M, Chen H, Yang B et al (2017) Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 267:69\u201384","journal-title":"Neurocomputing"},{"issue":"13","key":"7471_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107698","volume":"111","author":"R Bandyopadhyay","year":"2021","unstructured":"Bandyopadhyay R, Basu A, Cuevas E, Sarkar R (2021) Harris Hawk optimization with simulated annealing as a deep feature selection method for screening of covid-19 CT scans. Appl Soft Comput 111(13):107698","journal-title":"Appl Soft Comput"},{"issue":"1","key":"7471_CR55","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/s10462-020-09860-3","volume":"54","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Ding W, El-Shahat D (2021) A hybrid Harris Hawks optimization algorithm with simulated annealing for feature selection. Artif Intell Rev 54(1):593\u2013637","journal-title":"Artif Intell Rev"},{"key":"7471_CR56","unstructured":"DIARETDB1\u2014Standard Diabetic Retinopathy Database. https:\/\/www.it.lut.fi\/project\/imageret\/diaretdb1\/index.html. Accessed 17 Jun 2020"},{"key":"7471_CR57","unstructured":"DIARETDB0\u2014Standard Diabetic Retinopathy Database. https:\/\/www.it.lut.fi\/project\/imageret\/diaretdb0\/. Accessed 17 Jun 2020"},{"key":"7471_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2019.101839","volume":"58","author":"DJ Derwin","year":"2020","unstructured":"Derwin DJ, Selvi ST, Singh OJ, Shan BP (2020) A novel automated system of discriminating microaneurysms in fundus images. Biomed Signal Process Control 58:101839","journal-title":"Biomed Signal Process Control"},{"key":"7471_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2019.105815","volume":"121","author":"S Kumar","year":"2020","unstructured":"Kumar S, Adarsh A, Kumar B, Kumar A (2020) An automated early diabetic retinopathy detection through improved blood vessel and optic disc segmentation. Opt Laser Technol 121:105815","journal-title":"Opt Laser Technol"},{"key":"7471_CR60","doi-asserted-by":"publisher","first-page":"118164","DOI":"10.1109\/ACCESS.2020.3005152","volume":"8","author":"K Shankar","year":"2020","unstructured":"Shankar K, Zhang Y, Liu Y et al (2020) Hyperparameter tuning deep learning for diabetic retinopathy fundus image classification. IEEE Access 8:118164\u2013118173","journal-title":"IEEE Access"},{"key":"7471_CR61","first-page":"1444","volume":"XII","author":"N Rani","year":"2020","unstructured":"Rani N, Kaur J (2020) An evolutionary particle swarm optimization based classification technique for detection of diabetic retinopathy. J Xi\u2019an Univ Archit Technol XII:1444\u20131451","journal-title":"J Xi'an Univ Archit Technol"},{"key":"7471_CR62","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/s42452-020-2568-8","volume":"2","author":"K Shankar","year":"2020","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 2:748","journal-title":"SN Appl Sci"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07471-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07471-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07471-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T02:09:50Z","timestamp":1666318190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07471-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,21]]},"references-count":62,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["7471"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07471-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,21]]},"assertion":[{"value":"21 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}