{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:20:20Z","timestamp":1747153220049,"version":"3.40.5"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031205408"},{"type":"electronic","value":"9783031205415"}],"license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"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\/978-3-031-20541-5_14","type":"book-chapter","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T10:02:50Z","timestamp":1677492170000},"page":"295-305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Effective Diabetic Retinopathy Detection Using Hybrid Convolutional Neural Network Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7122-5790","authenticated-orcid":false,"given":"Niteesh","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7524-4064","authenticated-orcid":false,"given":"Rashad","family":"Ahmed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8939-4530","authenticated-orcid":false,"given":"B. H.","family":"Venkatesh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0310-4510","authenticated-orcid":false,"given":"M.","family":"Anand Kumar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"14_CR1","doi-asserted-by":"publisher","unstructured":"Bhatia, K., Arora, S., & Tomar, R. (2016). Diagnosis of diabetic retinopathy using machine learning classification algorithm. In 2016 2nd International Conference on Next Generation Computing Technologies (NGCT) (pp. 347\u2013351). https:\/\/doi.org\/10.1109\/NGCT.2016.7877439","DOI":"10.1109\/NGCT.2016.7877439"},{"key":"14_CR2","doi-asserted-by":"publisher","unstructured":"Boral, Y. S., & Thorat, S. S. (2021). Classification of diabetic retinopathy based on hybrid neural network. In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1354\u20131358). https:\/\/doi.org\/10.1109\/ICCMC51019.2021.9418224","DOI":"10.1109\/ICCMC51019.2021.9418224"},{"key":"14_CR3","doi-asserted-by":"publisher","unstructured":"Carrera, E. V., Gonz\u00e1lez, A., & Carrera, R. (2017). Automated detection of diabetic retinopathy using SVM. In 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON) (pp. 1\u20134). https:\/\/doi.org\/10.1109\/INTERCON.2017.8079692","DOI":"10.1109\/INTERCON.2017.8079692"},{"key":"14_CR4","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1177\/193229680900300315","volume":"3","author":"J Cuadros","year":"2009","unstructured":"Cuadros, J., Bresnick, G. (2009). EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. Journal of Diabetes Science and Technology, 3, 509\u2013516.","journal-title":"Journal of Diabetes Science and Technology"},{"key":"14_CR5","doi-asserted-by":"publisher","unstructured":"Harun, N. H., Yusof, Y., Hassan, F., & Embong, Z. (2019). Classification of fundus images for diabetic retinopathy using artificial neural network. In 2019 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology (JEEIT) (pp. 498\u2013501). https:\/\/doi.org\/10.1109\/JEEIT.2019.8717479","DOI":"10.1109\/JEEIT.2019.8717479"},{"key":"14_CR6","doi-asserted-by":"publisher","unstructured":"Herliana, A., Arifin, T., Susanti, S., & Hikmah, A. B. (2018). Feature selection of diabetic retinopathy disease using particle swarm optimization and neural network. In: 2018 6th International Conference on Cyber and IT Service Management (CITSM) (pp. 1\u20134). https:\/\/doi.org\/10.1109\/CITSM.2018.8674295","DOI":"10.1109\/CITSM.2018.8674295"},{"issue":"2","key":"14_CR7","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/72.991427","volume":"13","author":"C-W Hsu","year":"2002","unstructured":"Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415\u2013425. https:\/\/doi.org\/10.1109\/72.991427","journal-title":"IEEE Transactions on Neural Networks"},{"key":"14_CR8","doi-asserted-by":"publisher","unstructured":"Jayakumari, C., Lavanya, V., & Sumesh, E. P. (2020). Automated diabetic retinopathy detection and classification using ImageNet convolution neural network using fundus images. In: 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 577\u2013582). https:\/\/doi.org\/10.1109\/ICOSEC49089.2020.9215270","DOI":"10.1109\/ICOSEC49089.2020.9215270"},{"key":"14_CR9","doi-asserted-by":"publisher","unstructured":"Jiang, H., Xu, J., Shi, R., Yang, K., Zhang, D., Gao, M., Ma, H., & Qian, W. (2020). A multi-label deep learning model with interpretable Grad-CAM for diabetic retinopathy classification. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC) (pp. 1560\u20131563). https:\/\/doi.org\/10.1109\/EMBC44109.2020.9175884","DOI":"10.1109\/EMBC44109.2020.9175884"},{"key":"14_CR10","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems (Vol. 1, pp. 1097\u20131105). NIPS\u201912, Red Hook, NY, USA: Curran Associates."},{"key":"14_CR11","doi-asserted-by":"publisher","unstructured":"Kumar, S., & Kumar, B. (2012). Diabetic retinopathy detection by extracting area and number of microaneurysm from colour fundus image. In: 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 359\u2013364). https:\/\/doi.org\/10.1109\/SPIN.2018.8474264","DOI":"10.1109\/SPIN.2018.8474264"},{"key":"14_CR12","doi-asserted-by":"publisher","unstructured":"Ramani, R. G., Shanthamalar J., J., & Lakshmi, B. (2017). Automatic diabetic retinopathy detection through ensemble classification techniques automated diabetic retinopathy classification. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) (pp. 1\u20134). https:\/\/doi.org\/10.1109\/ICCIC.2017.8524342","DOI":"10.1109\/ICCIC.2017.8524342"},{"key":"14_CR13","doi-asserted-by":"publisher","unstructured":"Roy, A., Dutta, D., Bhattacharya, P., & Choudhury, S. (2017). Filter and fuzzy C means based feature extraction and classification of diabetic retinopathy using support vector machines. In: 2017 International Conference on Communication and Signal Processing (ICCSP) (pp. 1844\u20131848). https:\/\/doi.org\/10.1109\/ICCSP.2017.8286715","DOI":"10.1109\/ICCSP.2017.8286715"},{"key":"14_CR14","doi-asserted-by":"publisher","unstructured":"Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2016). Automated detection of neovascularization for proliferative diabetic retinopathy screening. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 1300\u20131303). https:\/\/doi.org\/10.1109\/EMBC.2016.7590945","DOI":"10.1109\/EMBC.2016.7590945"},{"key":"14_CR15","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. CoRR, abs\/1409.1556."},{"issue":"7","key":"14_CR16","first-page":"41","volume":"1","author":"P S Sodhu","year":"2014","unstructured":"Sodhu, P. S., & Khatkar, K. (2014). A hybrid approach for diabetic retinopathy analysis. International Journal of Computer Application and Technology, 1(7), 41\u201348.","journal-title":"International Journal of Computer Application and Technology"},{"key":"14_CR17","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1\u20139). https:\/\/doi.org\/10.1109\/CVPR.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"}],"container-title":["EAI\/Springer Innovations in Communication and Computing","Smart Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20541-5_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T10:06:21Z","timestamp":1677492381000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20541-5_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"ISBN":["9783031205408","9783031205415"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20541-5_14","relation":{},"ISSN":["2522-8595","2522-8609"],"issn-type":[{"type":"print","value":"2522-8595"},{"type":"electronic","value":"2522-8609"}],"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"10 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}