{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:50:17Z","timestamp":1742914217468,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819604364"},{"type":"electronic","value":"9789819604371"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-96-0437-1_14","type":"book-chapter","created":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T16:56:38Z","timestamp":1732640198000},"page":"185-195","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hypertension Classification for Fundus Image Based on Improving Clahe Morphology in Wavelet Transform and ResUNet"],"prefix":"10.1007","author":[{"given":"Tuyet","family":"Vo Thi Hong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nguyen Thanh","family":"Binh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"14_CR1","doi-asserted-by":"crossref","unstructured":"Syahputra, M.F., Aulia, I., Rahmat, R.F.: Hypertensive retinopathy identification from retinal fundus image using probabilistic neural network.\u00a0In: 2017 International Conference on Advanced Informatics, Concepts, Theory, and Applications (ICAICTA), Indonesia, pp. 1\u20136 (2017)","DOI":"10.1109\/ICAICTA.2017.8090989"},{"key":"14_CR2","doi-asserted-by":"crossref","unstructured":"Triwijoyo, B.K., Budiharto, W., Abdurachman, E.: The classification of hypertensive retinopathy using convolutional neural network. Procedia Comput. Sci. 116, 166\u2013173 (2017)","DOI":"10.1016\/j.procs.2017.10.066"},{"key":"14_CR3","unstructured":"Latha, M.A., Evangeline, N.C., SankaraNarayanan, S.: Colour image segmentation of fundus blood vessels for the detection of hypertensive retinopathy.\u00a0In: 2018 Fourth International Conference on Biosignals, Images and Instrumentation (ICBSII), India, pp. 206\u2013212 (2018)"},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Alam, M., Son, T., Toslak, D., Lim, J.I., Yao, X.: Combining ODR and blood vessel tracking for artery\u2013vein classification and analysis in color fundus images. Transl. Vis. Sci. Technol. 7(2), 23 (2018)","DOI":"10.1167\/tvst.7.2.23"},{"issue":"9","key":"14_CR5","doi-asserted-by":"publisher","first-page":"1446","DOI":"10.3390\/jcm8091446","volume":"8","author":"M Arsalan","year":"2019","unstructured":"Arsalan, M., Owais, M., Mahmood, T., Cho, S.W., Park, K.R.: Aiding the diagnosis of diabetic and hypertensive retinopathy using artificial intelligence-based semantic segmentation. J. Clin. Med. 8(9), 1446 (2019)","journal-title":"J. Clin. Med."},{"key":"14_CR6","doi-asserted-by":"crossref","unstructured":"Liang, Y., Chen, Z., Ward, R., Elgendi, M.: Hypertension assessment using photoplethysmography: a risk stratification approach. J. Clin. Med. DMPI 8(1), 12 (2019)","DOI":"10.3390\/jcm8010012"},{"issue":"4","key":"14_CR7","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3390\/diagnostics9040178","volume":"9","author":"W Chang","year":"2019","unstructured":"Chang, W., et al.: A machine-learning-based prediction method for hypertension outcomes based on medical data. Diagnostics 9(4), 178 (2019)","journal-title":"Diagnostics"},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Zhang, L., Yuan, M., An, Z., Zhao, X., Wu, H., Li, H., et al.: Prediction of hypertension, hyperglycemia and dyslipidemia from retinal fundus photographs via deep learning: a cross-sectional study of chronic diseases in central China. PLoS ONE 15(5), e0233166 (2020)","DOI":"10.1371\/journal.pone.0233166"},{"key":"14_CR9","doi-asserted-by":"crossref","unstructured":"Akram, M.U., Akbar, S., Hassan, T., Khawaja, S.G., Yasin, U. and Basit, I.: Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema. Data Brief 29, 105282 (2020)","DOI":"10.1016\/j.dib.2020.105282"},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"\u015e\u00fcyun, S.B., Ta\u015fdemir, \u015e., Bili\u015f, S., Milea, A.: Using a deep learning system that classifies hypertensive retinopathy based on the fundus images of patients of wide age. Traitement du Signal, IIETA, 38(1), 207\u2013213 (2021)","DOI":"10.18280\/ts.380122"},{"key":"14_CR11","doi-asserted-by":"publisher","first-page":"14078","DOI":"10.1109\/ACCESS.2021.3051085","volume":"9","author":"H Alhichri","year":"2021","unstructured":"Alhichri, H., Alswayed, A.S., Bazi, Y., Ammour, N., Alajlan, N.A.: Classification of remote sensing images using EfficientNet-B3 CNN model with attention. IEEE Access 9, 14078\u201314094 (2021)","journal-title":"IEEE Access"},{"issue":"1","key":"14_CR12","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3390\/jpm12010007","volume":"12","author":"M Arsalan","year":"2022","unstructured":"Arsalan, M., Haider, A., Choi, J., Park, K.R.: Diabetic and hypertensive retinopathy screening in fundus images using artificially intelligent shallow architectures. J. Personalized Med. 12(1), 7 (2022)","journal-title":"J. Personalized Med."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Mohan, N.J., Murugan, R., Goel, T.: Machine learning algorithms for hypertensive retinopathy detection through retinal fundus images, Chapter in Book Computer vision and recognition systems, 1st edition, Apple Academic Press (2022)","DOI":"10.1201\/9781003180593-3"},{"key":"14_CR14","doi-asserted-by":"crossref","unstructured":"Tuyet, V.T.H., Binh, N.T. and Tin, D.T.: A deep bottleneck u-net combined with saliency map for classifying diabetic retinopathy in fundus images.\u00a0Int. J. Online Biomed. Eng. 8(2), 105\u2013122 (2022)","DOI":"10.3991\/ijoe.v18i02.27605"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Sun, D., et al.: Chamber attention network (CAN): towards interpretable diagnosis of pulmonary artery hypertension using echocardiography. J. Adv. Res. 63, 103\u2013115 (2023)","DOI":"10.1016\/j.jare.2023.10.013"},{"issue":"8","key":"14_CR16","doi-asserted-by":"publisher","first-page":"4695","DOI":"10.3390\/app13084695","volume":"13","author":"D Nagpal","year":"2023","unstructured":"Nagpal, D., Alsubaie, N., Soufiene, B.O., Alqahtani, M.S., Abbas, M., Almohiy, H.M.: Automatic detection of diabetic hypertensive retinopathy in fundus images using transfer learning. Appl. Sci. 13(8), 4695 (2023)","journal-title":"Appl. Sci."},{"key":"14_CR17","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.3390\/bioengineering10091026","volume":"10","author":"M Shi","year":"2023","unstructured":"Shi, M., Zheng, Y., Wu, Y., Ren, Q.: Multitask attention-based neural network for intraoperative hypotension prediction. Bioengineering 10, 1026 (2023)","journal-title":"Bioengineering"},{"key":"14_CR18","doi-asserted-by":"crossref","unstructured":"Nizarudeen, S., Shanmughavel, G.R.:, Comparative analysis of ResNet, ResNet-SE, and attention-based RaNet for hemorrhage classification in CT images using deep learning. Biomed. Sig. Process. Control 88(Part A), 105672 (2024)","DOI":"10.1016\/j.bspc.2023.105672"},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Sivapriya, G., Manjula Devi, R., Keerthika, P., Praveen, V.: Automated diagnostic classification of diabetic retinopathy with microvascular structure of fundus images using deep learning method, Biomedical signal processing and control, Elsevier, 88(A): 105616, (2024)","DOI":"10.1016\/j.bspc.2023.105616"},{"key":"14_CR20","doi-asserted-by":"crossref","unstructured":"R. Gurthula, C. Vanukuru, V. Chiluka and M. S. G. L. Sumalata: detection of diabetic and hypertensive retinopathy using deep learning models.\u00a0In: 2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India, pp. 522\u2013527 (2024)","DOI":"10.1109\/ICAAIC60222.2024.10575049"},{"key":"14_CR21","doi-asserted-by":"crossref","unstructured":"Bhimavarapu, U., Chintalapudi, N., Battineni, G.: Automatic detection and classification of hypertensive retinopathy with improved convolution neural network and improved SVM. Bioengineering\u00a011(1), 56 (2024)","DOI":"10.3390\/bioengineering11010056"},{"key":"14_CR22","unstructured":"STARE dataset. https:\/\/cecas.clemson.edu\/~ahoover\/stare\/probing\/index.html, Accessed 15 Aug 2024"},{"key":"14_CR23","doi-asserted-by":"publisher","unstructured":"Osadchiy, A., Kamenev, A., Saharov, V., Chernyi, S.: Signal processing algorithm based on discrete wavelet transform.\u00a0Designs\u00a05(3), 41 (2021). https:\/\/doi.org\/10.3390\/designs5030041","DOI":"10.3390\/designs5030041"}],"container-title":["Communications in Computer and Information Science","Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0437-1_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,26]],"date-time":"2024-11-26T17:03:42Z","timestamp":1732640622000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0437-1_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819604364","9789819604371"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0437-1_14","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no conflicts of interest.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}},{"value":"FDSE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Future Data and Security Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Binh Duong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fdse2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/thefdse.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}