{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:57:14Z","timestamp":1743080234740,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031283499"},{"type":"electronic","value":"9783031283505"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-28350-5_10","type":"book-chapter","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:15:28Z","timestamp":1679879728000},"page":"122-134","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Short Review on Cataract Detection and Classification Approaches Using Distinct Ophthalmic Imaging Modalities"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5111-2040","authenticated-orcid":false,"given":"Aakash","family":"Garg","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2386-0532","authenticated-orcid":false,"given":"Jay Kant Pratap Singh","family":"Yadav","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2125-183X","authenticated-orcid":false,"given":"Sunita","family":"Yadav","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,19]]},"reference":[{"key":"10_CR1","unstructured":"WHO. World Report on Vision: Executive summary (2019). https:\/\/www.who.int\/docs\/. Accessed 04 June 2021"},{"key":"10_CR2","doi-asserted-by":"publisher","unstructured":"Vashist, P., Senjam, S.S., Gupta, V., Gupta, N., Kumar, A.: Definition of blindness under national program for control of blindness: do we need to revise it? Indian J Ophthalmol. 65(2), 92\u201396 (2017). https:\/\/doi.org\/10.4103\/ijo.IJO_869_16. PMID: 28345562; PMCID: PMC5381306","DOI":"10.4103\/ijo.IJO_869_16"},{"key":"10_CR3","doi-asserted-by":"publisher","unstructured":"Pathak, S., Raj, R., Singh, K., Verma, P.K., Kumar, B.: Development of portable and robust cataract detection and grading system by analyzing multiple texture features for Tele-Ophthalmology. Multimedia Tools Appl. 81(16), 23355\u201323371 (2022). https:\/\/doi.org\/10.1007\/s11042-022-12544-5","DOI":"10.1007\/s11042-022-12544-5"},{"key":"10_CR4","unstructured":"WHO. Global data on visual impairments (2012). https:\/\/www.who.int\/blindness\/. Accessed 04 June 2021"},{"key":"10_CR5","unstructured":"NPCBVI. National blindness and visual impairment survey India 2015-19: a summary report; (2020). https:\/\/npcbvi.gov.in\/writeReadData\/mainlinkFile\/File341.pdf. Accessed 14 June 2021"},{"issue":"2","key":"10_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41551-016-0024","volume":"1","author":"E Long","year":"2017","unstructured":"Long, E., et al.: An artificial intelligence platform for the multihospital collaborative management of congenital cataracts. Nat. Biomed. Eng. 1(2), 1\u20138 (2017)","journal-title":"Nat. Biomed. Eng."},{"key":"10_CR7","unstructured":"https:\/\/www.aao.org\/eye-health\/treatments\/what-is-slit-lamp. Accessed 06 June 2022"},{"key":"10_CR8","unstructured":"Wikipedia contributors. Fundus photography. In Wikipedia, The Free Encyclopedia (2022). https:\/\/en.wikipedia.org\/w\/index.php?title=Fundus_photography&oldid=1083927539. Accessed 06 June 2022"},{"issue":"13","key":"10_CR9","first-page":"1944","volume":"46","author":"CH Parikh","year":"2005","unstructured":"Parikh, C.H., Fowler, S., Davis, R.: Cataract screening using telemedicine and digital fundus photography. Investig. Ophthalmol. Vis. Sci. 46(13), 1944 (2005)","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"issue":"9","key":"10_CR10","doi-asserted-by":"publisher","first-page":"663","DOI":"10.4103\/0301-4738.194325","volume":"64","author":"B Raju","year":"2016","unstructured":"Raju, B., Raju, N.S.D., Akkara, J.D., Pathengay, A.: Do it yourself smartphone fundus camera \u2013 DIYretCAM. Indian J. Ophthalmol. 64(9), 663\u2013667 (2016). https:\/\/doi.org\/10.4103\/0301-4738.194325","journal-title":"Indian J. Ophthalmol."},{"key":"10_CR11","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13092-8","author":"A Sirajuddin","year":"2021","unstructured":"Sirajuddin, A., Balasubramanian, A., Karthikeyan, S.: Novel angular binary pattern (NABP) and kernel based convolutional neural networks classifiers for cataract detection. Multimedia Tools Appl. (2021). https:\/\/doi.org\/10.1007\/s11042-022-13092-8","journal-title":"Multimedia Tools Appl."},{"key":"10_CR12","doi-asserted-by":"publisher","unstructured":"Li, H., et al.: An automatic diagnosis system of nuclear cataract using slit-lamp images. In: 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, USA, pp. 3693\u20133696. IEEE (2009). https:\/\/doi.org\/10.1109\/IEMBS.2009.5334735","DOI":"10.1109\/IEMBS.2009.5334735"},{"issue":"7","key":"10_CR13","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1109\/TBME.2010.2041454","volume":"57","author":"H Li","year":"2010","unstructured":"Li, H., et al.: A computer-aided diagnosis system of nuclear cataract. IEEE Trans. Biomed. Eng. 57(7), 1690\u20131698 (2010)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-642-40763-5_58","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"Y Xu","year":"2013","unstructured":"Xu, Y., et al.: Automatic grading of nuclear cataracts from slit-lamp lens images using group sparsity regression. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8150, pp. 468\u2013475. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40763-5_58"},{"key":"10_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"458","DOI":"10.1007\/978-3-319-46726-9_53","volume-title":"Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016","author":"Y Xu","year":"2016","unstructured":"Xu, Y., Duan, L., Wong, D.W.K., Wong, T.Y., Liu, J.: Semantic reconstruction-based nuclear cataract grading from slit-lamp lens images. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 458\u2013466. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46726-9_53"},{"key":"10_CR16","doi-asserted-by":"publisher","unstructured":"Yang, M., Yang, J.J., Zhang, Q., Niu, Y., Li, J.: Classification of retinal image for automatic cataract detection. In: Proceedings of the 2013 IEEE 15th International Conference on e-Health Networking, Applications & Services (Healthcom 2013), Lisbon, pp. 674\u2013679 (2013). https:\/\/doi.org\/10.1109\/HealthCom.2013.6720761","DOI":"10.1109\/HealthCom.2013.6720761"},{"key":"10_CR17","doi-asserted-by":"publisher","unstructured":"Zheng, J., Guo, L., Peng, L., Li, J., Yang, J., Liang, Q.: Fundus image-based cataract classification. In: Proceedings of the IEEE International Conference on Imaging Systems and Techniques (IST), Santorini, Greece, pp. 90\u201394 (2014). https:\/\/doi.org\/10.1109\/IST.2014.6958452","DOI":"10.1109\/IST.2014.6958452"},{"key":"10_CR18","doi-asserted-by":"publisher","unstructured":"Fan, W., Shen, R., Zhang, Q., Yang, J.J., Li, J.: Principal component analysis-based cataract grading and classification. In: Proceedings of the 17th International Conference on E-Health Networking, Application & Services (HealthCom), Boston, MA, pp. 459\u2013462. IEEE (2015). https:\/\/doi.org\/10.1109\/HealthCom.2015.7454545","DOI":"10.1109\/HealthCom.2015.7454545"},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1016\/j.compind.2014.09.005","volume":"69","author":"L Guo","year":"2015","unstructured":"Guo, L., Yang, J.J., Peng, L., Li, J., Liang, Q.A.: Computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Comput. Ind. 69, 72\u201380 (2015). https:\/\/doi.org\/10.1016\/j.compind.2014.09.005","journal-title":"Comput. Ind."},{"key":"10_CR20","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cmpb.2015.10.007","volume":"124","author":"JJ Yang","year":"2016","unstructured":"Yang, J.J., et al.: Exploiting ensemble learning for automatic cataract detection and grading. Comput. Methods Programs Biomed. 124, 45\u201357 (2016). https:\/\/doi.org\/10.1016\/j.cmpb.2015.10.007","journal-title":"Comput. Methods Programs Biomed."},{"key":"10_CR21","doi-asserted-by":"publisher","unstructured":"Qiao, Z., Zhang, Q., Dong, Y., Yang, J.J.: Application of SVM based on genetic algorithm in classification of cataract fundus images. In: Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China. IEEE, pp. 1\u20135 (2017). https:\/\/doi.org\/10.1109\/IST.2017.8261541","DOI":"10.1109\/IST.2017.8261541"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Jagadale, A.B., Sonavane, S.S., Jadav, D.V.: Computer aided system for early detection of nuclear cataract using circle hough transform. In: Proceedings of the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Piscataway, NJ, USA, vol. 2019, pp. 1009\u20131012. IEEE (2019)","DOI":"10.1109\/ICOEI.2019.8862595"},{"key":"10_CR23","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1007\/978-3-319-60834-1_34","volume-title":"Proceedings of the Third International Afro-European Conference for Industrial Advancement\u2014AECIA 2016","author":"AA Khan","year":"2018","unstructured":"Khan, A.A., Akram, M.U., Tariq, A., Tahir, F., Wazir, K.: Automated computer aided detection of cataract. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A.M. (eds.) AECIA 2016. AISC, vol. 565, pp. 340\u2013349. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-60834-1_34"},{"issue":"2","key":"10_CR24","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/TMI.2019.2928229","volume":"39","author":"Y Zhou","year":"2019","unstructured":"Zhou, Y., Li, G., Li, H.: Automatic cataract classification using deep neural network with discrete state transition. IEEE Trans. Med. Imaging 39(2), 436\u2013446 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10_CR25","doi-asserted-by":"publisher","first-page":"2921","DOI":"10.1109\/TBME.2014.2335739","volume":"61","author":"M Caixinha","year":"2014","unstructured":"Caixinha, M., Jesus, D.A., Velte, E., Santos, M.J., Santos, J.B.: Using ultrasound backscattering signals and nakagami statistical distribution to assess regional cataract hardness. IEEE Trans. Biomed. Eng. 61(12), 2921\u20132929 (2014)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, L., Li, J., Han, H., Liu, B., Yang, J., Wang, Q.: Automatic cataract detection and grading using deep convolutional neural network. In: Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy, pp. 60\u201365. IEEE (2017). https:\/\/doi.org\/10.1109\/ICNSC.2017.8000068","DOI":"10.1109\/ICNSC.2017.8000068"},{"key":"10_CR27","doi-asserted-by":"publisher","unstructured":"Ran, J., Niu, K., He, Z., Zhang, H., Song, H.: Cataract detection and grading based on combination of deep convolutional neural network and random forests. In: 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), Guiyang, China. IEEE, pp. 155\u2013159 (2018). https:\/\/doi.org\/10.1109\/ICNIDC.2018.8525852","DOI":"10.1109\/ICNIDC.2018.8525852"},{"key":"10_CR28","doi-asserted-by":"publisher","unstructured":"Li, J., et al.: Automatic cataract diagnosis by image-based interpretability. In: Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, pp. 3964\u20133969 (2019). https:\/\/doi.org\/10.1109\/SMC.2018.00672","DOI":"10.1109\/SMC.2018.00672"},{"issue":"4","key":"10_CR29","doi-asserted-by":"publisher","first-page":"1450","DOI":"10.1111\/coin.12518","volume":"38","author":"JKPS Yadav","year":"2022","unstructured":"Yadav, J.K.P.S., Yadav, S.: Computer-aided diagnosis of cataract severity using retinal fundus images and deep learning. Comput. Intell. 38(4), 1450\u20131473 (2022). https:\/\/doi.org\/10.1111\/coin.12518","journal-title":"Comput. Intell."},{"key":"10_CR30","doi-asserted-by":"publisher","unstructured":"Xiong, Y., He, Z., Niu, K., Zhang, H., Song, H.: Automatic cataract classification based on multi-feature fusion and SVM. In: Proceedings of the 2018 IEEE 4th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 1557\u20131561. IEEE (2018). https:\/\/doi.org\/10.1109\/CompComm.2018.8780617","DOI":"10.1109\/CompComm.2018.8780617"},{"issue":"6","key":"10_CR31","doi-asserted-by":"publisher","first-page":"691","DOI":"10.1080\/21681163.2020.1806733","volume":"8","author":"A Imran","year":"2020","unstructured":"Imran, A., Li, J., Pei, Y., Akhtar, F., Yang, J.J., Dang, Y.: Automated identification of cataract severity using retinal fundus images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. 8(6), 691\u2013698 (2020). https:\/\/doi.org\/10.1080\/21681163.2020.1806733","journal-title":"Comput. Methods Biomech. Biomed. Eng. Imaging Vis."},{"issue":"11","key":"10_CR32","doi-asserted-by":"publisher","first-page":"2693","DOI":"10.1109\/TBME.2015.2444389","volume":"62","author":"X Gao","year":"2015","unstructured":"Gao, X., Lin, S., Wong, T.Y.: Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans. Biomed. Eng. 62(11), 2693\u20132701 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Qian, X., Patton, E.W., Swaney, J., Xing, Q., Zeng, T.: Machine learning on cataracts classification using squeeze net. In: Proceedings of the 2018 4th International Conference on Universal Village (UV), Piscataway, NJ, USA, vol. 2, pp. 1\u20133. IEEE (2018)","DOI":"10.1109\/UV.2018.8642133"},{"issue":"7","key":"10_CR34","first-page":"474","volume":"61","author":"D Peterson","year":"2020","unstructured":"Peterson, D., Ho, P., Chong, J.: Detecting cataract using smartphone. Invest. Ophthalmol. Vis. Sci. 61(7), 474 (2020)","journal-title":"Invest. Ophthalmol. Vis. Sci."}],"container-title":["Lecture Notes in Computer Science","Big Data Analytics in Astronomy, Science, and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-28350-5_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T01:24:23Z","timestamp":1679880263000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-28350-5_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031283499","9783031283505"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-28350-5_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Big Data Analytics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bigda2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/web-ext.u-aizu.ac.jp\/labs\/is-ds\/BDA2022-Aizu.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"70","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"20% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2,5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}