{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:49:47Z","timestamp":1776901787379,"version":"3.51.2"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032167071","type":"print"},{"value":"9783032167088","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-16708-8_3","type":"book-chapter","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:24:21Z","timestamp":1776900261000},"page":"37-49","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MultiH-EU: An AI-Driven Decision Support System for\u00a0Multi-disease Diagnosis"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5126-6480","authenticated-orcid":false,"given":"Diogen","family":"Babuc","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3143-8908","authenticated-orcid":false,"given":"Teodor-Florin","family":"Forti\u015f","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"3_CR1","unstructured":"Babuc, D.: An authentic algorithm for ciphering and deciphering called Latin Djokovic. arXiv preprint arXiv:2403.01463 (2024)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Babuc, D., Babuc, G.: Das-Alz: Alzheimer\u2019s disease classification using downscaled MRI scans and a reinforced convolutional neural network. In: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE), pp.\u00a01\u20137. IEEE (2024)","DOI":"10.1109\/BIBE63649.2024.10820486"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Babuc, D., Forti\u015f, A.E.: A customizable intelligent system for cervical cytology image classifications. In: International Conference on Complex, Intelligent, and Software Intensive Systems, pp. 82\u201393. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-70011-8_8"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Babuc, D., Forti\u015f, A.E.: Edovit-Alz: Alzheimer\u2019s disease identification with vision transformer using extremely downscaled MRI data. In: International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, pp. 109\u2013120. Springer, Cham (2024)","DOI":"10.1007\/978-3-031-76462-2_10"},{"key":"3_CR5","doi-asserted-by":"crossref","unstructured":"Babuc, D., Forti\u015f, T.F.: Federated learning platforms for privacy-preserving histopathological image classification. In: International Conference on Advanced Information Networking and Applications, pp. 317\u2013328. Springer, Cham (2025)","DOI":"10.1007\/978-3-031-87769-8_28"},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Babuc, D., Forti\u015f, T.F.: Hybrid task scheduling for optimized neural network inference on skin lesions in resource-constrained systems. In: Proceedings of the 5th Workshop on Machine Learning and Systems, pp. 269\u2013275 (2025)","DOI":"10.1145\/3721146.3721933"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Babuc, D., Iva\u015fcu, T., Ardelean, M., Onchi\u015f, D.: Bionnica: a deep neural network architecture for colorectal polyps\u2019 premalignancy risk evaluation. medRxiv, pp. 2024\u201306 (2024)","DOI":"10.1101\/2024.06.19.24309153"},{"key":"3_CR8","unstructured":"Babuc, D., Onchis, D.: Bovnet: Cervical cells classifications using a custom-based neural network with autoencoders"},{"key":"3_CR9","unstructured":"Beutel, D.J., et\u00a0al.: Flower: a friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Farca\u015f, A., Lobon\u0163, A., Babuc, D., Iva\u015fcu, T., \u015etef\u0103nig\u0103, S.A.: EI-vit-net: a vision transformer approach for diabetic retinopathy\u2019s decision support pipeline. In: 2024 IEEE 24th International Conference on Bioinformatics and Bioengineering (BIBE), pp.\u00a01\u20136. IEEE (2024)","DOI":"10.1109\/BIBE63649.2024.10820483"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Gangwar, A.K., Ravi, V.: Diabetic retinopathy detection using transfer learning and deep learning. In: Evolution in Computational Intelligence: Frontiers in Intelligent Computing: Theory and Applications (FICTA 2020), vol. 1, pp. 679\u2013689. Springer, Cham (2021)","DOI":"10.1007\/978-981-15-5788-0_64"},{"issue":"2","key":"3_CR12","doi-asserted-by":"publisher","first-page":"21539","DOI":"10.48084\/etasr.9874","volume":"15","author":"K Gidijala","year":"2025","unstructured":"Gidijala, K., Sagenela, V.K.: The conception of fundus multi-disease dataset (FMDD) using multi-spectral generative adversarial networks. Eng. Technol. Appl. Sci. Res. 15(2), 21539\u201321544 (2025)","journal-title":"Eng. Technol. Appl. Sci. Res."},{"issue":"1","key":"3_CR13","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.neuroimage.2009.05.056","volume":"48","author":"C Hinrichs","year":"2009","unstructured":"Hinrichs, C., et al.: Spatially augmented LPboosting for ad classification with evaluations on the ADNI dataset. Neuroimage 48(1), 138\u2013149 (2009)","journal-title":"Neuroimage"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: Kvasir-seg: a segmented polyp dataset. In: International Conference on Multimedia Modeling, pp. 451\u2013462. Springer, Cham (2019)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"114822","DOI":"10.1109\/ACCESS.2020.3003890","volume":"8","author":"MA Kassem","year":"2020","unstructured":"Kassem, M.A., Hosny, K.M., Fouad, M.M.: Skin lesions classification into eight classes for ISIC 2019 using deep convolutional neural network and transfer learning. IEEE Access 8, 114822\u2013114832 (2020)","journal-title":"IEEE Access"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Koski, E., Murphy, J.: AI in healthcare. In: Nurses and Midwives in the Digital Age, pp. 295\u2013299. IOS Press (2021)","DOI":"10.3233\/SHTI210726"},{"key":"3_CR17","unstructured":"Kothinti, R.R.: Artificial intelligence in disease prediction: transforming early diagnosis and preventive healthcare (2024)"},{"issue":"10","key":"3_CR18","doi-asserted-by":"publisher","DOI":"10.2196\/20891","volume":"22","author":"GH Lee","year":"2020","unstructured":"Lee, G.H., Shin, S.Y.: Federated learning on clinical benchmark data: performance assessment. J. Med. Internet Res. 22(10), e20891 (2020)","journal-title":"J. Med. Internet Res."},{"issue":"3","key":"3_CR19","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1111\/j.1541-0420.2006.00731.x","volume":"63","author":"S Ma","year":"2007","unstructured":"Ma, S., Huang, J.: Combining multiple markers for classification using ROC. Biometrics 63(3), 751\u2013757 (2007)","journal-title":"Biometrics"},{"issue":"3","key":"3_CR20","doi-asserted-by":"publisher","first-page":"1023","DOI":"10.3390\/make5030053","volume":"5","author":"M Mohammad Amini","year":"2023","unstructured":"Mohammad Amini, M., Jesus, M., Fanaei Sheikholeslami, D., Alves, P., Hassanzadeh Benam, A., Hariri, F.: Artificial intelligence ethics and challenges in healthcare applications: a comprehensive review in the context of the European GDPR mandate. Mach. Learn. Knowl. Extraction 5(3), 1023\u20131035 (2023)","journal-title":"Mach. Learn. Knowl. Extraction"},{"key":"3_CR21","unstructured":"Ram, E., Stihl, E.: Impact of cell type selection on binary classification of cervical cancer using convolutional neural networks: a compatibility analysis of herlev and sipakmed (2023)"},{"key":"3_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/978-3-030-60548-3_20","volume-title":"Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning","author":"S Silva","year":"2020","unstructured":"Silva, S., Altmann, A., Gutman, B., Lorenzi, M.: Fed-BioMed: a general open-source frontend framework for federated learning in healthcare. In: Albarqouni, S., et al. (eds.) DART\/DCL -2020. LNCS, vol. 12444, pp. 201\u2013210. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60548-3_20"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Tariq, M., Palade, V., Ma, Y.: Transfer learning based classification of diabetic retinopathy on the kaggle eyepacs dataset. In: International Conference on Medical Imaging and Computer-Aided Diagnosis, pp. 89\u201399. Springer, Cham (2022)","DOI":"10.1007\/978-981-16-6775-6_8"},{"key":"3_CR24","unstructured":"TS, C., Jagadale, B.N.: Comparative analysis of u-net and deeplab for automatic polyp segmentation in colonoscopic frames using CVC-clinicdb dataset (2023)"},{"issue":"1","key":"3_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/sdata.2018.161","volume":"5","author":"P Tschandl","year":"2018","unstructured":"Tschandl, P., Rosendahl, C., Kittler, H.: The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci. Data 5(1), 1\u20139 (2018)","journal-title":"Sci. Data"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Wu, C.Y., Gupta, P., Mohapatra, S.: Case studies and recommendations for designing federated learning models for digital healthcare systems. In: Federated Learning for Digital Healthcare Systems, pp. 301\u2013323. Elsevier (2024)","DOI":"10.1016\/B978-0-443-13897-3.00007-2"},{"issue":"2","key":"3_CR27","first-page":"489","volume":"128","author":"S Xie","year":"2021","unstructured":"Xie, S., Yu, Z., Lv, Z.: Multi-disease prediction based on deep learning: a survey. Comput. Model. Eng. Sci. 128(2), 489\u2013522 (2021)","journal-title":"Comput. Model. Eng. Sci."},{"key":"3_CR28","unstructured":"Xie, S., Zheng, H., Liu, C., Lin, L.: SNAS: stochastic neural architecture search. arXiv preprint arXiv:1812.09926 (2018)"},{"key":"3_CR29","unstructured":"Zhaoyang, S., et al.: Exploring zero-shot learning for multi-disease diagnosis using vision transformers with tongue and facial imaging. Available at SSRN 4987853"}],"container-title":["Communications in Computer and Information Science","Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-16708-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T23:24:24Z","timestamp":1776900264000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-16708-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032167071","9783032167088"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-16708-8_3","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HC_AIxIA_HYDRA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Joint Workshop on Artificial Intelligence for Healthcare, and Hybrid Models for Coupling Deductive and Inductive Reasoning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bologna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hc_aixia_hydra2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/unical.it\/hcaixia-hydra-2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}