{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T15:13:41Z","timestamp":1774624421052,"version":"3.50.1"},"reference-count":178,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T00:00:00Z","timestamp":1670457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Diabetic retinopathy occurs due to long-term diabetes with changing blood glucose levels and has become the most common cause of vision loss worldwide. It has become a severe problem among the working-age group that needs to be solved early to avoid vision loss in the future. Artificial intelligence-based technologies have been utilized to detect and grade diabetic retinopathy at the initial level. Early detection allows for proper treatment and, as a result, eyesight complications can be avoided. The in-depth analysis now details the various methods for diagnosing diabetic retinopathy using blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages. In most trials, fundus images of the retina are used, which are taken using a fundus camera. This survey discusses the basics of diabetes, its prevalence, complications, and artificial intelligence approaches to deal with the early detection and classification of diabetic retinopathy. The research also discusses artificial intelligence-based techniques such as machine learning and deep learning. New research fields such as transfer learning using generative adversarial networks, domain adaptation, multitask learning, and explainable artificial intelligence in diabetic retinopathy are also considered. A list of existing datasets, screening systems, performance measurements, biomarkers in diabetic retinopathy, potential issues, and challenges faced in ophthalmology, followed by the future scope conclusion, is discussed. To the author, no other literature has analyzed recent state-of-the-art techniques considering the PRISMA approach and artificial intelligence as the core.<\/jats:p>","DOI":"10.3390\/bdcc6040152","type":"journal-article","created":{"date-parts":[[2022,12,9]],"date-time":"2022-12-09T01:51:26Z","timestamp":1670550686000},"page":"152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["A Systematic Literature Review on Diabetic Retinopathy Using an Artificial Intelligence Approach"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3077-4395","authenticated-orcid":false,"given":"Pooja","family":"Bidwai","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India"}]},{"given":"Shilpa","family":"Gite","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India"}]},{"given":"Kishore","family":"Pahuja","sequence":"additional","affiliation":[{"name":"Natasha Eye Care and Research Centre, Pimple Saudagar, Pune 411027, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2653-3780","authenticated-orcid":false,"given":"Ketan","family":"Kotecha","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence and Machine Learning, Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis Institute of Technology, Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, India"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,8]]},"reference":[{"key":"ref_1","unstructured":"Khatri, M. (2022, May 18). Diabetes Complications. Available online: https:\/\/www.webmd.com\/diabetes\/diabetes-complications."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1586\/eop.12.52","article-title":"Diabetic Retinopathy Management Guidelines","volume":"7","author":"Chakrabarti","year":"2012","journal-title":"Expert Rev. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Early Treatment Diabetic Retinopathy Study Research Group (2020). Grading diabetic retinopathy from stereoscopic color fundus photographs- an extension of the modified Airlie House classification. Ophthalmology, 127, S99\u2013S119.","DOI":"10.1016\/j.ophtha.2020.01.030"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Scanlon, P.H., Wilkinson, C.P., Aldington, S.J., and Matthews, D.R. (2009). A Practical Manual of Diabetic Retinopathy Management, Wiley-Blackwell. [1st ed.].","DOI":"10.1002\/9781444308174"},{"key":"ref_5","unstructured":"Ravelo, J.L. (2022, January 03). Aging and Population Growth, Challenges for Vision Care: WHO Report. Available online: https:\/\/www.devex.com\/news\/aging-and-population-growth-challenges-for-vision-care-who-report-95763."},{"key":"ref_6","unstructured":"WHO (2022, January 03). World Report on Vision, 2019. Available online: https:\/\/www.who.int\/publications\/i\/item\/9789241516570."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"841","DOI":"10.4103\/jfmpc.jfmpc_218_18","article-title":"India achieves WHO recommended doctor population ratio: A call for a paradigm shift in public health discourse!","volume":"7","author":"Kumar","year":"2018","journal-title":"J. Fam. Med. Prim. Care"},{"key":"ref_8","unstructured":"WHO (2022, May 05). Global Data on Visual Impairment. Available online: http:\/\/www.who.int\/blindness\/GLOBALDATAFINALforweb.pdf."},{"key":"ref_9","unstructured":"Centers for Disease Control and Prevention (2022, May 10). Common Eye Disorders and Diseases, Available online: https:\/\/www.cdc.gov\/visionhealth\/basics\/ced\/index.html."},{"key":"ref_10","unstructured":"Malik, U. (2022, April 03). Most Common Eye Problems\u2014Signs, Symptoms and Treatment Options. Available online: https:\/\/irisvision.com\/most-common-eye-problems-signs-symptoms-and-treatment\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.nima.2006.08.134","article-title":"Computer aided diagnosis based on medical image processing and artificial intelligence methods","volume":"569","author":"Stoitsis","year":"2006","journal-title":"Nucl. Instrum. Methods Phys. Res. Sect. A"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"012049","DOI":"10.1088\/1757-899X\/1070\/1\/012049","article-title":"Detection of diabetic retinopathy using deep learning methodology","volume":"1070","author":"Mushtaq","year":"2021","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Taylor, R., and Batey, D. (2012). Handbook of Retinal Screening in Diabetes: Diagnosis and Management. Handbook of Retinal Screening in Diabetes: Diagnosis and Management, Wiley-Blackwell. [2nd ed.].","DOI":"10.1002\/9781119968573"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1016\/j.procs.2018.05.074","article-title":"Diabetic Retinopathy: Present and Past","volume":"132","author":"Gupta","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15209","DOI":"10.1007\/s11042-018-7044-8","article-title":"Diabetic retinopathy detection through artificial intelligent techniques: A review and open issues","volume":"79","author":"Ishtiaq","year":"2019","journal-title":"Multimedia Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16173","DOI":"10.1007\/s11042-019-07751-6","article-title":"Retinal image quality assessment for diabetic retinopathy screening: A survey","volume":"79","author":"Lin","year":"2020","journal-title":"Multimedia Tools Appl."},{"key":"ref_17","first-page":"2705","article-title":"Glaucoma Detection in Retinal Images Using Image Processing Techniques: A Survey","volume":"7","author":"Qureshi","year":"2015","journal-title":"Int. J. Adv. Netw. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yin, Y., Shi, J., Fang, W., Li, H., and Wang, X. (2017, January 11\u201313). Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_31"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1136\/bjo.2008.151126","article-title":"Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy","volume":"94","author":"Scotland","year":"2010","journal-title":"Br. J. Ophthalmol."},{"key":"ref_20","unstructured":"(2022, May 20). Difference between Normal Vision and DR Vision. Available online: https:\/\/www.researchgate.net\/publication\/350930649_DRISTI_a_hybrid_deep_neural_network_for_diabetic_retinopathy_diagnosis\/figures?lo=1."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","article-title":"Convolutional neural networks for diabetic retinopathy","volume":"90","author":"Pratt","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100377","DOI":"10.1016\/j.imu.2020.100377","article-title":"Diabetic retinopathy detection through deep learning techniques: A review","volume":"20","author":"Alyoubi","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1038\/s41433-020-0811-6","article-title":"MultiColor imaging to detect different subtypes of retinal microaneurysms in diabetic retinopathy","volume":"35","author":"Arrigo","year":"2020","journal-title":"Eye"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yasin, S., Iqbal, N., Ali, T., Draz, U., Alqahtani, A., Irfan, M., Rehman, A., Glowacz, A., Alqhtani, S., and Proniewska, K. (2021). Severity Grading and Early Retinopathy Lesion Detection through Hybrid Inception-ResNet Architecture. Sensors, 21.","DOI":"10.3390\/s21206933"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2019.04.019","article-title":"An end-to-end unified framework for multi-lesion segmentation offundus images","volume":"349","author":"Guo","year":"2019","journal-title":"Neurocomput"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1007\/s11892-013-0393-9","article-title":"Automated Analysis of Diabetic Retinopathy Images: Principles, Recent Developments, and Emerging Trends","volume":"13","author":"Li","year":"2013","journal-title":"Curr. Diabetes Rep."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mishra, A., Singh, L., and Pandey, M. (2021, January 19\u201320). Short Survey on machine learning techniques used for diabetic retinopathy detection. Proceedings of the IEEE 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS51004.2021.9397142"},{"key":"ref_29","first-page":"1","article-title":"Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images","volume":"11","author":"Oh","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s13534-017-0047-y","article-title":"Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy","volume":"8","author":"Mansour","year":"2017","journal-title":"Biomed. Eng. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Pepose, J.S. (2014). A prospective randomized clinical evaluation of 3 presbyopia-correcting intraocular lenses after cataract extraction. Am. J. Ophthalmol., 3\u20139.","DOI":"10.1016\/j.ajo.2014.06.003"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"e1557","DOI":"10.7717\/peerj.1557","article-title":"Trends and topics in eye disease research in PubMed from 2010 to 2014","volume":"4","author":"Boudry","year":"2016","journal-title":"PeerJ"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1136\/bjo.80.11.940","article-title":"ORIGINAL ARTICLES-Clinical science Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool","volume":"80","author":"Gardner","year":"1996","journal-title":"Br. J. Ophthalmol."},{"key":"ref_35","first-page":"1","article-title":"Assessment of image quality on color fundus retinal images using the automatic retinal image analysis","volume":"12","author":"Shi","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"557","DOI":"10.3233\/THC-130759","article-title":"An automated retinal imaging method for the early diagnosis of diabetic retinopathy","volume":"21","author":"Franklin","year":"2013","journal-title":"Technol. Health Care"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1097\/CM9.0000000000001816","article-title":"Artificial intelligence for diabetic retinopathy","volume":"135","author":"Li","year":"2021","journal-title":"Chin. Med. J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1515\/comp-2020-0222","article-title":"An effective integrated machine learning approach for detecting diabetic retinopathy","volume":"12","author":"Pragathi","year":"2022","journal-title":"Open Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.knosys.2019.03.016","article-title":"Automated identification and grading system of diabetic retinopathy using deep neural networks","volume":"175","author":"Zhang","year":"2019","journal-title":"Knowl. Based Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"101701","DOI":"10.1016\/j.artmed.2019.07.009","article-title":"Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey","volume":"99","author":"Asiri","year":"2019","journal-title":"Artif. Intell. Med."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kamal, M.M., Shanto, M.H.I., Mirza Mahmud Hossan, M., Hasnat, A., Sultana, S., and Biswas, M. (2022). A Comprehensive Review on the Diabetic Retinopathy, Glaucoma and Strabismus Detection Techniques Based on Machine Learning and Deep Learning. Eur. J. Med. Health Sci., 24\u201340.","DOI":"10.34104\/ejmhs.022.024040"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.jns.2016.11.043","article-title":"Family carers\u2019 experiences of receiving the news of a diagnosis of Motor Neurone Disease: A national survey","volume":"372","author":"Aoun","year":"2017","journal-title":"J. Neurol. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Khade, S., Ahirrao, S., Phansalkar, S., Kotecha, K., Gite, S., and Thepade, S.D. (2021). Iris Liveness Detection for Biometric Authentication: A Systematic Literature Review and Future Directions. Inventions, 6.","DOI":"10.3390\/inventions6040065"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41433-019-0566-0","article-title":"Artificial intelligence for diabetic retinopathy screening: A review","volume":"34","author":"Grzybowski","year":"2020","journal-title":"Eye"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1159\/000368426","article-title":"Screening for Diabetic Retinopathy in the Central Region of Portugal. Added Value of Automated \u2018Disease\/No Disease\u2019 Grading","volume":"233","author":"Ribeiro","year":"2015","journal-title":"Ophthalmologica"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2134254","DOI":"10.1001\/jamanetworkopen.2021.34254","article-title":"Pivotal Evaluation of an Artificial Intelligence System for Autonomous Detection of Referrable and Vision-Threatening Diabetic Retinopathy","volume":"4","author":"Ipp","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1167\/iovs.02-0417","article-title":"Automated Detection of Diabetic Retinopathy in a Fundus Photographic Screening Population","volume":"44","author":"Larsen","year":"2003","journal-title":"Investig. Opthalmology Vis. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images from Multiethnic Populations With Diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_50","first-page":"1","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"39","author":"Lavin","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"840024","DOI":"10.3389\/fmed.2022.883462","article-title":"Evaluation of an Artificial Intelligence System for the Detection of Diabetic Retinopathy in Chinese Community Healthcare Centers","volume":"9","author":"Dong","year":"2022","journal-title":"Front. Med."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Wu, J., Xin, J., Hong, L., You, J., and Zheng, N. (2015, January 25\u201329). New hierarchical approach for microaneurysms detection with matched filter and machine learning. Proceedings of the 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy.","DOI":"10.1109\/EMBC.2015.7319351"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Biyani, R.S., and Patre, B.M. (2016, January 6\u20138). A clustering approach for exudates detection in screening of diabetic retinopathy. Proceedings of the 2016 International Conference on Signal and Information Processing (IConSIP), Nanded, India.","DOI":"10.1109\/ICONSIP.2016.7857495"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.compbiomed.2015.07.003","article-title":"Referral system for hard exudates in eye fundus","volume":"64","author":"Naqvi","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1097\/ICU.0000000000000470","article-title":"Deep learning applications in ophthalmology","volume":"29","author":"Rahimy","year":"2018","journal-title":"Curr. Opin. Ophthalmol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"615","DOI":"10.13005\/bpj\/1148","article-title":"Diabetic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy","volume":"10","author":"Sisodia","year":"2017","journal-title":"Biomed. Pharmacol. J."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1186\/s12938-017-0414-z","article-title":"Automatic non-proliferative diabetic retinopathy screening system based on color fundus image","volume":"16","author":"Xiao","year":"2017","journal-title":"Biomed. Eng. Online"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.cmpb.2016.10.017","article-title":"Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels","volume":"138","author":"Srivastava","year":"2017","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_59","unstructured":"Vanithamani, R.R.C.R., and Renee Christina, R. (2016). Exudates in detection and classification of diabetic retinopathy. International Conference on Soft Computing and Pattern Recognition, Springer. Available online: https:\/\/books.google.co.in\/books?id=hFNuDwAAQBAJ&pg=PA108&lpg=PA108&dq=Vanithamani+R,+Renee+Christina+R+(2018)+Exudates+in+detection+and+classification+of+diabetic+retinopathy:+252\u2013261&source=bl&ots=CWbiXEy9bP&sig=ACfU3U1vfBvwrh06MvSJbSKzMp8Sl2Cm4w&hl=en&."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"990","DOI":"10.1109\/TBME.2016.2585344","article-title":"Localizing Microaneurysms in Fundus Images Through Singular Spectrum Analysis","volume":"64","author":"Wang","year":"2016","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Nijalingappa, P., and Sandeep, B. (2016, January 29\u201331). Machine learning approach for the identification of diabetes retinopathy and its stages. Proceedings of the 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT), Davangere, India.","DOI":"10.1109\/ICATCCT.2015.7456965"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Xiao, D., Yu, S., Vignarajan, J., An, D., Tay-Kearney, M.-L., and Kanagasingam, Y. (2017, January 11\u201315). Retinal hemorrhage detection by rule-based and machine learning approach. Proceedings of the 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Republic of Korea.","DOI":"10.1109\/EMBC.2017.8036911"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Almotiri, J., Elleithy, K., and Elleithy, A. (2018). Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8020155"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Bui, T., Maneerat, N., and Watchareeruetai, U. (2017, January 11\u201312). Detection of cotton wool for diabetic retinopathy analysis using neural network. Proceedings of the IEEE 10th International Workshop on Computational Intelligence and Applications, Hiroshima, Japan.","DOI":"10.1109\/IWCIA.2017.8203585"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.bbe.2014.01.004","article-title":"Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images","volume":"34","author":"Franklin","year":"2014","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_66","first-page":"311","article-title":"Diabetic Rethinopathy Screening by Bright Lesions Extraction from Fundus Images","volume":"64","author":"Oravec","year":"2013","journal-title":"J. Electr. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"2511","DOI":"10.3233\/IFS-141224","article-title":"Hierarchical classifier for soft and hard exudates detection of retinal fundus images","volume":"27","author":"Kavitha","year":"2014","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Paing, M.P., Choomchuay, S., and Yodprom, M.D.R. (2016, January 7\u20139). Detection of lesions and classification of diabetic retinopathy using fundus images. Proceedings of the 2016 9th Biomedical engineering international conference (BMEiCON), Laung Prabang, Laos.","DOI":"10.1109\/BMEiCON.2016.7859642"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"2563","DOI":"10.1109\/ACCESS.2017.2671918","article-title":"Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method","volume":"5","author":"Zhou","year":"2017","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.cmpb.2018.02.011","article-title":"Hard exudates segmentation based on learned initial seeds and iterative graph cut","volume":"158","author":"Kusakunniran","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Khade, S., Gite, S., Thepade, S.D., Pradhan, B., and Alamri, A. (2021). Detection of Iris Presentation Attacks Using Feature Fusion of Thepade\u2019s Sorted Block Truncation Coding with Gray-Level Co-Occurrence Matrix Features. Sensors, 21.","DOI":"10.3390\/s21217408"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Wen, L. (2020). Algorithms: A Comparative Study of Bagging, Boosting and Stacking Techniques. Remote Sens, 12.","DOI":"10.3390\/rs12101683"},{"key":"ref_73","unstructured":"Brownlee, J. (2020). Stacking Ensemble Machine Learning with Python. Machine Learning Mastery, Machine Learning Mastery. Available online: https:\/\/machinelearningmastery.com\/stacking-ensemble-machine-learning-with-python\/."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.3233\/JIFS-169226","article-title":"Hybrid classifier and region-dependent integrated features for detection of diabetic retinopathy","volume":"32","author":"Mane","year":"2017","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.bspc.2017.02.012","article-title":"Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification","volume":"35","author":"Fraz","year":"2017","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.cmpb.2016.09.018","article-title":"Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion","volume":"137","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1504\/IJBET.2014.062743","article-title":"Early detection and classification of microaneurysms in retinal fundus images using sequential learning methods","volume":"15","author":"Bala","year":"2014","journal-title":"Int. J. Biomed. Eng. Technol."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2264","DOI":"10.1587\/transinf.E92.D.2264","article-title":"Comparative Analysis of Automatic Exudate Detection between Machine Learning and Traditional Approaches","volume":"92","author":"Sopharak","year":"2009","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Srinivasan, R., Surya, J., Ruamviboonsuk, P., Chotcomwongse, P., and Raman, R. (2022). Influence of Different Types of Retinal Cameras on the Performance of Deep Learning Algorithms in Diabetic Retinopathy Screening. Life, 12.","DOI":"10.3390\/life12101610"},{"key":"ref_80","unstructured":"Valarmathi, S., and Vijayabhanu, R. (2021, January 25\u201327). A Survey on Diabetic Retinopathy Disease Detection and Classification using Deep Learning Techniques. Proceedings of the 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII, Chennai, India."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Wang, X., Lu, Y., Wang, Y., and Chen, W.-B. (2018, January 7\u20139). Diabetic Retinopathy Stage Classification Using Convolutional Neural Networks. Proceedings of the 2018 IEEE 19th International Conference on Information Reuse and Integration for Data Science, IRI, Salt Lake City, UT, USA.","DOI":"10.1109\/IRI.2018.00074"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.cmpb.2018.02.016","article-title":"Microaneurysm detection using fully convolutional neural networks","volume":"158","author":"Chudzik","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Yan, Y., Gong, J., and Liu, Y. (2019, January 3\u20135). A Novel Deep Learning Method for Red Lesions Detection Using Hybrid Feature. Proceedings of the 31st Chinese Control and Decision Conference, CCDC, Nanchang, China.","DOI":"10.1109\/CCDC.2019.8833190"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","article-title":"Deep learning for visual understanding: A review","volume":"187","author":"Guo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"20415","DOI":"10.1007\/s11042-017-5438-7","article-title":"Video scene analysis: An overview and challenges on deep learning algorithms","volume":"77","author":"Abbas","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.2991\/ijcis.d.210316.001","article-title":"A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification","volume":"14","author":"Gurcan","year":"2021","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Khade, S., Gite, S., and Pradhan, B. (2022). Iris Liveness Detection Using Multiple Deep Convolution Networks. Big Data Cogn. Comput., 6.","DOI":"10.3390\/bdcc6020067"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Ketkar, N., and Moolayil, J. (2021). Deep Learning with Python, Manning Publications.","DOI":"10.1007\/978-1-4842-5364-9"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Olivas, E.S., Guerrero, J.D.M., Martinez-Sober, M., Magdalena-Benedito, J.R., and Serrano, L. (2010). Magdalena-Benedito, Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Global.","DOI":"10.4018\/978-1-60566-766-9"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Masood, S., Luthra, T., Sundriyal, H., and Ahmed, M. (2017, January 5\u20136). Identification of diabetic retinopathy in eye images using transfer learning. Proceedings of the International Conference on Computing, Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/CCAA.2017.8229977"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Xu, X., Lin, J., Tao, Y., and Wang, X. (December, January 30). An Improved DenseNet Method Based on Transfer Learning for Fundus Medical Images. Proceedings of the 2018 7th international conference on digital home (ICDH), Guilin, China.","DOI":"10.1109\/ICDH.2018.00033"},{"key":"ref_92","first-page":"68","article-title":"Deep Convolutional Neural Networks for Diabetic Retinopathy Classification","volume":"72","author":"Lian","year":"2018","journal-title":"ACM Int. Conf. Proceeding Ser."},{"key":"ref_93","unstructured":"Blakely, M. (2022, April 07). \u2018The Importance of Sight and Vision,\u2019 Marvel Optics. Available online: https:\/\/www.marveloptics.com\/blog\/the-importance-of-sight-and-vision-molly-blakely\/."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.media.2017.04.012","article-title":"Deep image mining for diabetic retinopathy screening","volume":"39","author":"Quellec","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.eswa.2018.06.034","article-title":"Retinal vessel segmentation based on Fully Convolutional Neural Networks","volume":"112","author":"Oliveira","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.cmpb.2017.10.017","article-title":"An ensemble deep learning based approach for red lesion detection in fundus images","volume":"153","author":"Orlando","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.compeleceng.2015.01.013","article-title":"Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms","volume":"45","author":"Mahendran","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1109\/TBME.2005.862571","article-title":"On the Adaptive Detection of Blood Vessels in Retinal Images","volume":"53","author":"Wu","year":"2006","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.media.2009.05.005","article-title":"Retinal image analysis based on mixture models to detect hard exudates","volume":"13","author":"Mayo","year":"2009","journal-title":"Med. Image Anal."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.cmpb.2008.07.006","article-title":"Neural network based detection of hard exudates in retinal images","volume":"93","author":"Hornero","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.medengphy.2007.04.010","article-title":"A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis","volume":"30","author":"Hornero","year":"2008","journal-title":"Med. Eng. Phys."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1016\/j.media.2012.06.003","article-title":"A multiple-instance learning framework for diabetic retinopathy screening","volume":"16","author":"Quellec","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.cmpb.2011.06.007","article-title":"Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images","volume":"107","year":"2012","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.media.2011.07.004","article-title":"Exudate-based diabetic macular edema detection in fundus images using publicly available datasets","volume":"16","author":"Giancardo","year":"2012","journal-title":"Med. Image Anal."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.ins.2012.03.003","article-title":"Sparse Representation Classifier for microaneurysm detection and retinal blood vessel extraction","volume":"200","author":"Zhang","year":"2012","journal-title":"Inf. Sci."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.cviu.2011.09.001","article-title":"Combining algorithms for automatic detection of optic disc and macula in fundus images","volume":"116","author":"Qureshi","year":"2012","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_107","first-page":"251","article-title":"Decision support system for diabetic retinopathy using discrete wavelet transform","volume":"227","author":"Noronha","year":"2012","journal-title":"Proc. Inst. Mech. Eng. Part H J. Eng. Med."},{"key":"ref_108","first-page":"530","article-title":"A Hybrid SVM NA\u00cfVE-BAYES Classifier for Bright Lesions Recognition in Eye Fundus Images","volume":"13","author":"Gharaibeh","year":"2021","journal-title":"Int. J. Electr. Eng. Inform."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1504\/IJSISE.2018.093825","article-title":"An effective image processing method for detection of diabetic retinopathy diseases from retinal fundus images","volume":"11","author":"Nahar","year":"2018","journal-title":"Int. J. Signal Imaging Syst. Eng."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.patcog.2012.07.002","article-title":"Identification and classification of microaneurysms for early detection of diabetic retinopathy","volume":"46","author":"Akram","year":"2013","journal-title":"Pattern Recognit."},{"key":"ref_111","unstructured":"Nagabhusan, T.N., Sundararajan, N., and Suresh, S. (2016, January 12\u201313). Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy. Proceedings of the 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysuru, India."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Umapathy, A., Sreenivasan, A., Nairy, D.S., Natarajan, S., and Rao, B.N. (2019, January 7\u20139). Image Processing, Textural Feature Extraction and Transfer Learning based detection of Diabetic Retinopathy. Proceedings of the 2019 9th International Conference on Bioscience, Biochemistry and Bioinformatics, Singapore.","DOI":"10.1145\/3314367.3314376"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"103537","DOI":"10.1016\/j.compbiomed.2019.103537","article-title":"Diabetic retinopathy detection using red lesion localization and convolutional neural networks","volume":"116","author":"Zago","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., and Qian, W. (2019, January 23\u201327). An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857160"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1109\/RBME.2010.2084567","article-title":"Retinal Imaging and Image Analysis","volume":"3","author":"Garvin","year":"2010","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated Identification of Diabetic Retinopathy Using Deep Learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Doshi, D., Shenoy, A., Sidhpura, D., and Gharpure, P. (2016, January 11). Diabetic retinopathy detection using deep convolutional neural networks. Proceedings of the 2016 International Conference on Computing, Analytics and Security Trends (CAST), Pune, India.","DOI":"10.1109\/CAST.2016.7914977"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Ghosh, R., Ghosh, K., and Maitra, S. (2017, January 26\u201327). Automatic detection and classification of diabetic retinopathy stages using CNN. Proceedings of the 2017 4th International Conference on Signal Processing and Integrated Networks (SPIN), Delhi, India.","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Gondal, W.M., Kohler, J.M., Grzeszick, R., Fink, G.A., and Hirsch, M. (2017, January 17\u201320). Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. Proceedings of the 2017 IEEE international conference on image processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296646"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Wu, H., and Dong, J. (2017, January 20\u201322). Automatic Screening of Diabetic Retinopathy Images with Convolution Neural Network Based on Caffe Framework. Proceedings of the 1st International Conference on Medical and Health Informatics 2017, Taichung city, Taiwan.","DOI":"10.1145\/3107514.3107523"},{"key":"ref_121","first-page":"138","article-title":"Weighted ensemble based automatic detection of exudates in fundus photographs","volume":"2014","author":"Prentasic","year":"2014","journal-title":"IEEE"},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Roy, P., Tennakoon, R., Cao, K., Sedai, S., Mahapatra, D., Maetschke, S., and Garnavi, R. (2017, January 18\u201321). A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images. Proceedings of the 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), Melbourne, Australia.","DOI":"10.1109\/ISBI.2017.7950703"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Xu, K., Feng, D., and Mi, H. (2017). Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Molecules, 22.","DOI":"10.3390\/molecules22122054"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Yang, Y., Li, T., Li, W., Wu, H., Fan, W., and Zhang, W. (2017). Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-66179-7_61"},{"key":"ref_125","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1109\/TMI.2016.2526689","article-title":"Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images","volume":"35","author":"Hoyng","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1007\/s13755-017-0034-9","article-title":"A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm","volume":"5","author":"Budak","year":"2017","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_127","first-page":"1","article-title":"Automatic Microaneurysms Detection Based on Multifeature Fusion Dictionary Learning","volume":"2017","author":"Zhou","year":"2017","journal-title":"Comput. Math. Methods Med."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.knosys.2016.11.022","article-title":"Performance analysis of descriptive statistical features in retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier fusion","volume":"118","author":"Barkana","year":"2017","journal-title":"Knowl. Based Syst."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Dasgupta, A., and Singh, S. (2017, January 18\u201321). A fully convolutional neural network based structured prediction approach towards the retinal vessel segmentation. Proceedings of the 14th International Symposium on Biomedical Imaging (ISBI), Melbourne, VIC, Australia.","DOI":"10.1109\/ISBI.2017.7950512"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"2181","DOI":"10.1007\/s11548-017-1619-0","article-title":"Multi-level deep supervised networks for retinal vessel segmentation","volume":"12","author":"Mo","year":"2017","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.jocs.2017.02.006","article-title":"Segmentation of optic disc, fovea, and retinal vasculature using a single convolutional neural network","volume":"20","author":"Tan","year":"2017","journal-title":"J. Comput. Sci."},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.compbiomed.2014.12.016","article-title":"Retinal vessel extraction using lattice neural networks with dendritic processing","volume":"58","author":"Vega","year":"2015","journal-title":"Comput. Biol. Med."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.neucom.2014.07.059","article-title":"Hierarchical retinal blood vessel segmentation based on feature and ensemble learning. Neurocomputing","volume":"149","author":"Wang","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Choi, J.Y., Yoo, T.K., Seo, J.G., Kwak, J., Um, T.T., and Rim, T.H. (2017). Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0187336"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1007\/s13410-017-0561-6","article-title":"Automatic detection of retinal hemorrhages by exploiting image processing techniques for screening retinal diseases in diabetic patients","volume":"38","author":"Mumtaz","year":"2017","journal-title":"Int. J. Diabetes Dev. Ctries."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1515\/bmt-2015-0188","article-title":"Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images","volume":"61","author":"Santhi","year":"2016","journal-title":"Biomed. Eng. Biomed. Tech."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Li, G., Zheng, S., and Li, X. (2018). Exudate Detection in Fundus Images via Convolutional Neural Network. International Forum on Digital TV and Wireless Multimedia Communications, Springer.","DOI":"10.1007\/978-981-10-8108-8_18"},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1142\/S0218339014500156","article-title":"A Sequential learning method for detection and classification of exudates in retinal images to assess diabetic retinopathy","volume":"22","author":"Bala","year":"2014","journal-title":"J. Biol. Syst."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1007\/s00521-015-1929-5","article-title":"Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening","volume":"27","author":"Rahim","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Omar, M., Khelifi, F., and Tahir, M.A. (2016, January 6\u20138). Detection and classification of retinal fundus images exudates using region based multiscale LBP texture approach. Proceedings of the 2016 International Conference on Control, Decision and Information Technologies (CoDIT), Saint Julian\u2019s, Malta.","DOI":"10.1109\/CoDIT.2016.7593565"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1007\/s11517-017-1638-6","article-title":"Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features","volume":"55","author":"Abbas","year":"2017","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Ouyang, W., Luo, P., Zeng, X., Qiu, S., Tian, Y., Li, H., Yang, S., Wang, Z., Xiong, Y., and Qian, C. (2014). Deepid-net: Multi-stage and deformable deep convolutional neural networks for object detection. arXiv.","DOI":"10.1109\/CVPR.2015.7298854"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Shan, J., and Li, L. (2016, January 27\u201329). A deep learning method for microaneurysm detection in fundus images. Proceedings of the IEEE First International Conference on Connected Health: Applications, Systems, and Engineering Technologies, Washington, DC, USA.","DOI":"10.1109\/CHASE.2016.12"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1007\/978-3-319-67934-1_2","article-title":"Early Stage Detection of Diabetic Retinopathy Using an Optimal Feature Set","volume":"Volume 678","author":"Shirbahadurkar","year":"2017","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s10916-017-0853-x","article-title":"A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy","volume":"41","author":"SK","year":"2017","journal-title":"J. Med. Syst."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"5200","DOI":"10.1167\/iovs.16-19964","article-title":"Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning","volume":"57","author":"Lou","year":"2016","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.knosys.2013.12.023","article-title":"An ensemble-based system for automatic screening of diabetic retinopathy","volume":"60","author":"Antal","year":"2014","journal-title":"Knowl. Based Syst."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Carrera, E.V., Gonzalez, A., and Carrera, R. (2017, January 15\u201318). Automated detection of diabetic retinopathy using SVM. Proceedings of the IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, Peru.","DOI":"10.1109\/INTERCON.2017.8079692"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.compbiomed.2017.07.007","article-title":"A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis","volume":"88","author":"Marin","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_150","doi-asserted-by":"crossref","unstructured":"Li, X., Pang, T., Xiong, B., Liu, W., Liang, P., and Wang, T. (2017, January 14\u201316). Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. Proceedings of the 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Shanghai, China.","DOI":"10.1109\/CISP-BMEI.2017.8301998"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1007\/s00521-015-2059-9","article-title":"Multi-retinal disease classification by reduced deep learning features","volume":"28","author":"Arunkumar","year":"2017","journal-title":"Neural Comput. Applic."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","article-title":"Automated segmentation of exudates, hemorrhages, and microaneurysms using a single convolutional neural network","volume":"420","author":"Tan","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., and Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for the improved staging of diabetic retinopathy. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179790"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1080\/03772063.2016.1221745","article-title":"Diabetic Retinopathy Diagnosis in Retinal Images Using Hopfield Neural Network","volume":"62","author":"Hemanth","year":"2016","journal-title":"IETE J. Res."},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"104450","DOI":"10.1016\/j.compbiomed.2021.104450","article-title":"A comprehensive review and analysis of supervised-learning and soft computing techniques for stress diagnosis in humans","volume":"134","author":"Sharma","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Lakshminarayanan, V., Kheradfallah, H., Sarkar, A., and Balaji, J.J. (2021). Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. J. Imaging, 7.","DOI":"10.3390\/jimaging7090165"},{"key":"ref_157","first-page":"209","article-title":"Confusion Matrix","volume":"61","author":"Shultz","year":"2011","journal-title":"Encycl. Mach. Learn."},{"key":"ref_158","unstructured":"Wikipedia, F. (2022, March 12). Cohen Kappa. Available online: https:\/\/thenewstack.io\/cohens-kappa-what-it-is-when-to-use-it-and-how-to-avoid-its-pitfalls."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, C., Porta, M., Bandello, F., Grauslund, J., Harding, S.P., Aldington, S.J., Egan, C., Frydkjaer-Olsen, U., Garc\u00eda-Arum\u00ed, J., and Gibson, J. (2020). The Usefulness of Serum Biomarkers in the Early Stages of Diabetic Retinopathy: Results of the EUROCONDOR Clinical Trial. J. Clin. Med., 9.","DOI":"10.3390\/jcm9041233"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1080\/08820538.2021.1893351","article-title":"Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records","volume":"36","author":"Jacoba","year":"2021","journal-title":"Semin. Ophthalmol."},{"key":"ref_161","first-page":"250","article-title":"HHS Public Access","volume":"36","author":"Records","year":"2022","journal-title":"Biomarkers"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1016\/S0161-6420(95)30973-6","article-title":"Progression of Retinopathy with Intensive versus Conventional Treatment in the Diabetes Control and Complications Trial","volume":"102","author":"Control","year":"1995","journal-title":"Ophthalmology"},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1056\/NEJMoa1001288","article-title":"Effects of Medical Therapies on Retinopathy Progression in Type 2 Diabetes","volume":"363","author":"Group","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1001\/jamaophthalmol.2013.5024","article-title":"Challenges in elucidating the genetics of diabetic retinopathy","volume":"132","author":"Kuo","year":"2014","journal-title":"JAMA Ophthalmol."},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.preteyeres.2014.07.003","article-title":"Role of microRNAs in the modulation of diabetic retinopathy","volume":"43","author":"Mastropasqua","year":"2014","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"1403","DOI":"10.1161\/CIRCRESAHA.110.223552","article-title":"Epigenetics","volume":"107","author":"Cooper","year":"2010","journal-title":"Circ. Res."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/623619","article-title":"Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers","volume":"2015","author":"Torok","year":"2015","journal-title":"J. Diabetes Res."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.abb.2012.11.004","article-title":"Proteomic analysis of retinopathy-related plasma biomarkers in diabetic patients","volume":"529","author":"Lu","year":"2013","journal-title":"Arch. Biochem. Biophys."},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.cca.2011.01.025","article-title":"Correlations of six related pyrimidine metabolites and diabetic retinopathy in Chinese type 2 diabetic patients","volume":"412","author":"Xia","year":"2011","journal-title":"Clin. Chim. Acta"},{"key":"ref_170","unstructured":"Hussain, F., Hussain, R., and Hossain, E. (2021). Explainable Artificial Intelligence (XAI): An Engineering Perspective. arXiv, Available online: http:\/\/arxiv.org\/abs\/2101.03613."},{"key":"ref_171","first-page":"104","article-title":"Thiery, Explainable diabetic retinopathy classification based on neural-symbolic learning","volume":"2986","author":"Jang","year":"2021","journal-title":"CEUR Workshop Proc."},{"key":"ref_172","first-page":"1","article-title":"Explainable Artificial Intelligence\u2013A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review","volume":"133","author":"Deshpande","year":"2022","journal-title":"Comput. Model. Eng. Sci."},{"key":"ref_173","unstructured":"Leopold, H.A., Singh, A., Sengupta, S., Zelek, J.S., and Lakshminarayanan, V. (2020). Recent advances in deep learning applications for retinal diagnosis using OCT. 2020. State of the Art in Neural Networks, Elsevier."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12938-022-01018-2","article-title":"Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital","volume":"21","author":"Liu","year":"2022","journal-title":"Biomed. Eng. Online"},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Nguyen, D.M.H., Mai, T.T.N., Than, N.T.T., Prange, A., and Sonntag, D. (2021). Self-supervised Domain Adaptation for Diabetic Retinopathy Grading Using Vessel Image Reconstruction. German Conference on Artificial Intelligence (K\u00fcnstliche Intelligenz), Springer.","DOI":"10.1007\/978-3-030-87626-5_26"},{"key":"ref_176","doi-asserted-by":"crossref","unstructured":"Song, R., Cao, P., Yang, J., Zhao, D., and Zaiane, O.R. (2020, January 16\u201319). A Domain Adaptation Multi-instance Learning for Diabetic Retinopathy Grading on Retinal Images. Proceedings of the 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Seoul, Republic of Korea.","DOI":"10.1109\/BIBM49941.2020.9313398"},{"key":"ref_177","unstructured":"Crawshaw, M. (2020). Multi-task learning with deep neural networks: A survey. arXiv."},{"key":"ref_178","first-page":"13267","article-title":"Multi-Task Learning for Diabetic Retinopathy Grading and Lesion Segmentation","volume":"34","author":"Foo","year":"2020","journal-title":"Proc. Conf. AAAI Artif. Intell."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/152\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:36:21Z","timestamp":1760146581000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/6\/4\/152"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,8]]},"references-count":178,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["bdcc6040152"],"URL":"https:\/\/doi.org\/10.3390\/bdcc6040152","relation":{},"ISSN":["2504-2289"],"issn-type":[{"value":"2504-2289","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,8]]}}}