{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T15:29:13Z","timestamp":1772465353704,"version":"3.50.1"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T00:00:00Z","timestamp":1772409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah Bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2025R140"],"award-info":[{"award-number":["PNURSP2025R140"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"DOI":"10.1007\/s44196-026-01188-0","type":"journal-article","created":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T06:34:18Z","timestamp":1772433258000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Secure Cloud-Based Hybrid Deep Learning for Early Diagnosis of Ovarian Cancer Using Federated Learning"],"prefix":"10.1007","volume":"19","author":[{"given":"Umesh Kumar","family":"Lilhore","sequence":"first","affiliation":[]},{"given":"Sarita","family":"Simaiya","sequence":"additional","affiliation":[]},{"given":"G.","family":"Lakshmi Narayanan","sequence":"additional","affiliation":[]},{"given":"Anto Lourdu Xavier Raj Arockia","family":"Selvarathinam","sequence":"additional","affiliation":[]},{"given":"A.","family":"Rasmi","sequence":"additional","affiliation":[]},{"given":"Lidia Gosy","family":"Tekeste","sequence":"additional","affiliation":[]},{"given":"Ehab Seif","family":"Ghith","sequence":"additional","affiliation":[]},{"given":"Heba G.","family":"Mohamed","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,2]]},"reference":[{"key":"1188_CR1","doi-asserted-by":"crossref","unstructured":"Wei, Y. F., Ning, L., Xu, Y. L., Ma, J., Li, D. R., Feng, Z. F., Liu, F.H., Li, Y.Z., Xu, H.L., Li, P., Yu, Y.P., Lang, J. H.: Worldwide patterns and trends in ovarian cancer incidence by histological subtype: a population-based analysis from 1988 to 2017. EClinicalMedicine 79 (2025)","DOI":"10.1016\/j.eclinm.2024.102983"},{"key":"1188_CR2","doi-asserted-by":"crossref","unstructured":"Naderi Yaghouti, A.R., Shalbaf, A., Alizadehsani, R., Tan, R.S., Vijayananthan, A., Yeong, C.H., Acharya, U.R. Artificial intelligence for ovarian cancer detection with medical images: a review of the last decade (2013\u20132023). Arch. Comput. Methods Eng. 1\u201332 (2025)","DOI":"10.1007\/s11831-025-10268-x"},{"key":"1188_CR3","doi-asserted-by":"crossref","unstructured":"Fazilath, M., Umasankar, P.: Comprehensive analysis of artificial intelligence applications for early detection of ovarian tumours: current trends and future directions. In: 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS), pp. 1\u20139. IEEE (2025)","DOI":"10.1109\/ICICACS65178.2025.10968627"},{"key":"1188_CR4","doi-asserted-by":"crossref","unstructured":"Ziyambe, B., Yahya, A., Mushiri, T., Tariq, M.U., Abbas, Q., Babar, M., Albathan, M., Asim, M., Hussain, A., Jabbar, S.. A deep learning framework for the prediction and diagnosis of ovarian cancer in pre-and post-menopausal women. Diagnostics 13(10):1703. (2023)","DOI":"10.3390\/diagnostics13101703"},{"issue":"1","key":"1188_CR5","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1007\/s12672-025-02416-3","volume":"16","author":"X Zeng","year":"2025","unstructured":"Zeng, X., Li, Z., Dai, L., Li, J., Liao, L., Chen, W.: Machine learning in ovarian cancer: a bibliometric and visual analysis from 2004 to 2024. Discov. Oncol. 16(1), 755 (2025)","journal-title":"Discover Oncol."},{"issue":"1","key":"1188_CR6","doi-asserted-by":"publisher","first-page":"18","DOI":"10.31662\/jmaj.2024-0203","volume":"8","author":"M Komatsu","year":"2025","unstructured":"Komatsu, M., Teraya, N., Natsume, T., Harada, N., Takeda, K., Hamamoto, R.: Clinical application of artificial intelligence in ultrasound imaging for oncology. JMA J. 8(1), 18\u201325 (2025)","journal-title":"JMA J."},{"key":"1188_CR7","doi-asserted-by":"crossref","unstructured":"Fahim, T.A., Alam, F.B., Ahmmed, K.T.: OVANet: a dual attention mechanism-based new deep learning framework for diagnosis and classification of ovarian cancer subtypes from histopathological images. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3460077"},{"key":"1188_CR8","doi-asserted-by":"crossref","unstructured":"Radhakrishnan, M., Sampathila, N., Muralikrishna, H., Swathi, K.S.: Advancing ovarian cancer diagnosis through deep learning and explainable AI: a multiclassification approach. IEEE Access (2024)","DOI":"10.1109\/ACCESS.2024.3448219"},{"key":"1188_CR9","unstructured":"UBC-OCEAN dataset: Accessed 10 Apr 2024. https:\/\/www.kaggle.com\/datasets\/gunesevitan\/ubc-ocean-dataset"},{"issue":"1","key":"1188_CR10","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1007\/s13721-024-00454-5","volume":"13","author":"MJ Sundari","year":"2024","unstructured":"Sundari, M.J., Brintha, N.C.: TLOD: innovative ovarian tumor detection for accurate multiclass classification and clinical application. Netw. Model. Anal. Health Inf. Bioinf. 13(1), 18 (2024)","journal-title":"Netw. Model. Anal. Health Inf. Bioinf."},{"key":"1188_CR11","doi-asserted-by":"crossref","unstructured":"Al Huda, M.S., Arman, R.A., Shrestha, T.E., Tamim, S.A., Ali, M.A.: DeepResVit: a hybrid deep learning approach for ovarian cancer classification with XAI. In: 2024 2nd International Conference on Information and Communication Technology (ICICT), pp. 229\u2013233. IEEE (2024)","DOI":"10.1109\/ICICT64387.2024.10839719"},{"key":"1188_CR12","doi-asserted-by":"crossref","unstructured":"Sowmiya, S., Umapathy, S., Alhajlah, O., Almutairi, F., Aslam, S., Ahalya, R.K.: F-Net: Follicles Net, an efficient tool for the diagnosis of polycystic ovarian syndrome using deep learning techniques. PLOS ONE 19(8), e0307571 (2024)","DOI":"10.1371\/journal.pone.0307571"},{"key":"1188_CR13","doi-asserted-by":"crossref","unstructured":"Ghorbian, M., Ghorbian, S.: Ovarian cancer detection using IoT-based intelligent assistant and blockchain technology. In: Internet of Things Enabled Machine Learning for Biomedical Applications, pp. 97\u2013115. CRC Press (2024)","DOI":"10.1201\/9781003487647-6"},{"issue":"1","key":"1188_CR14","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1186\/s12880-024-01251-2","volume":"24","author":"Y Du","year":"2024","unstructured":"Du, Y., Guo, W., Xiao, Y., Chen, H., Yao, J., Wu, J.: Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study. BMC Med. Imaging 24(1), 89 (2024)","journal-title":"BMC Med. Imaging"},{"issue":"7","key":"1188_CR15","doi-asserted-by":"publisher","first-page":"966","DOI":"10.1007\/s42979-024-03272-8","volume":"5","author":"C Kamala","year":"2024","unstructured":"Kamala, C., Shivaram, J.M.: Early detection of benign ovarian tumor classification using U-NET+ with hybrid deep learning techniques. SN Comput. Sci. 5(7), 966 (2024)","journal-title":"SN Comput. Sci."},{"issue":"7","key":"1188_CR16","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s00432-024-05879-z","volume":"150","author":"SK Behera","year":"2024","unstructured":"Behera, S.K., Das, A., Sethy, P.K.: Deep fine-KNN classification of ovarian cancer subtypes using efficientNet-B0 extracted features: a comprehensive analysis. J. Cancer Res. Clin. Oncol. 150(7), 361 (2024)","journal-title":"J. Cancer Res. Clin. Oncol."},{"key":"1188_CR17","doi-asserted-by":"publisher","first-page":"102691","DOI":"10.1016\/j.artmed.2023.102691","volume":"146","author":"S Sharma","year":"2023","unstructured":"Sharma, S., Guleria, K.: A comprehensive review on federated learning based models for healthcare applications. Artif. Intell. Med. 146, 102691 (2023)","journal-title":"Artif. Intell. Med."},{"key":"1188_CR18","doi-asserted-by":"crossref","unstructured":"Lavanya, J.M.S., Subbulakshmi, P.: Artificial intelligence prediction of the risk of ovarian cancer at an early stage in smart cities. In: Healthcare-Driven Intelligent Computing Paradigms to Secure Futuristic Smart Cities, pp. 251\u2013267. Chapman and Hall\/CRC (2024)","DOI":"10.1201\/9781032631738-15"},{"key":"1188_CR19","doi-asserted-by":"crossref","unstructured":"Sadeghi, M.H., Sina, S., Omidi, H., Farshchitabrizi, A.H., Alavi, M.: Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities. Pol. J. Radiol. 89, e30 (2024)","DOI":"10.5114\/pjr.2024.134817"},{"key":"1188_CR20","doi-asserted-by":"publisher","first-page":"11527","DOI":"10.1007\/s10586-017-1416-0","volume":"22","author":"G Zhang","year":"2019","unstructured":"Zhang, G., Kou, L., Yuan, Y., Sun, J., Lin, Y., Da, Q., Wang, W.: An intelligent method of cancer prediction based on mobile cloud computing. Cluster Comput. 22, 11527\u201311535 (2019)","journal-title":"Cluster Comput."},{"issue":"7","key":"1188_CR21","doi-asserted-by":"publisher","first-page":"1651","DOI":"10.3390\/cancers14071651","volume":"14","author":"CW Wang","year":"2022","unstructured":"Wang, C.W., Lee, Y.C., Chang, C.C., Lin, Y.J., Liou, Y.A., Hsu, P.C., Chang, C.C., Sai, A.K.O., Wang, C.H., Chao, T.K.: A weakly supervised deep learning method for guiding ovarian cancer treatment and identifying an effective biomarker. Cancers 14(7), 1651 (2022)","journal-title":"Cancers"},{"key":"1188_CR22","doi-asserted-by":"publisher","unstructured":"Chawla, G., Rizvi, S.W.A.: Healthcare data security in a cloud environment. In: International Conference on Intelligent Vision and Computing, pp. 245\u2013253. Springer Nature (2022). https:\/\/doi.org\/10.1007\/978-3-030-88227-7_22","DOI":"10.1007\/978-3-030-88227-7_22"},{"key":"1188_CR23","doi-asserted-by":"crossref","unstructured":"Siddiqui, S., Khan, A.A., Siddiqui, M.S., Dey, I.: Enhanced accuracy and real-time monitoring: a hybrid communication architecture for fertility monitoring. J. Comput. Netw. Commun. 2024, 2336628 (2024)","DOI":"10.1155\/2024\/2336628"},{"key":"1188_CR24","doi-asserted-by":"publisher","first-page":"107065","DOI":"10.1016\/j.bspc.2024.107065","volume":"100","author":"SI Priyadharshini","year":"2025","unstructured":"Priyadharshini, S.I., Irene, D.S., Beulah, J.R., Ponnuviji, N.P.: Enhancing real-time health monitoring with a hybrid recurrent long short-term tyrannosaurus search for menstrual cups. Biomed. Signal Process. Control. 100, 107065 (2025)","journal-title":"Biomed. Signal Process. Control"},{"issue":"1","key":"1188_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21608\/njccs.2025.351020.1038","volume":"9","author":"TE Ibrahim","year":"2025","unstructured":"Ibrahim, T.E., Saraya, M.S., Saleh, A.I., Rabie, A.H.: Attended CNN-LSTM for predicting bladder cancer recurrence and response to treatments. Nile J. Commun. Comput. Sci. 9(1), 1\u201316 (2025)","journal-title":"Nile J. Communication Comput. Sci."},{"issue":"1","key":"1188_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44196-024-00712-4","volume":"18","author":"R Natarajan","year":"2025","unstructured":"Natarajan, R., Krishna, S., Gururaj, H.L., Flammini, F., Alfurhood, B.S., Kumar, C.M.: A novel hybrid dynamic Harris Hawks optimized gated recurrent unit approach for breast cancer prediction. Int. J. Comput. Intell. Syst. 18(1), 1\u201316 (2025)","journal-title":"Int. J. Comput. Intell. Syst."},{"issue":"21","key":"1188_CR27","doi-asserted-by":"publisher","first-page":"15805","DOI":"10.1007\/s00521-023-08569-y","volume":"35","author":"HH Maria","year":"2023","unstructured":"Maria, H.H., Jossy, A.M., Malarvizhi, S.: A hybrid deep learning approach for detection and segmentation of ovarian tumours. Neural Comput. Appl. 35(21), 15805\u201315819 (2023)","journal-title":"Neural Comput. Appl."},{"key":"1188_CR28","doi-asserted-by":"crossref","unstructured":"Faruqui, N., Yousuf, M.A., Kateb, F.A., Hamid, M.A., Monowar, M.M.: Healthcare as a service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis. Heliyon 9(11), e11512 (2023)","DOI":"10.1016\/j.heliyon.2023.e21520"},{"issue":"17","key":"1188_CR29","doi-asserted-by":"publisher","first-page":"2813","DOI":"10.3390\/diagnostics13172813","volume":"13","author":"L Escudero Sanchez","year":"2023","unstructured":"Escudero Sanchez, L., Buddenkotte, T., Sa\u2019d, A., McCague, M., Darcy, C., Rundo, J., Samoshkin, L., A., et al.: Integrating artificial intelligence tools in the clinical research setting: the ovarian cancer use case. Diagnostics 13(17), 2813 (2023)","journal-title":"Diagnostics"},{"issue":"24","key":"1188_CR30","doi-asserted-by":"publisher","first-page":"4137","DOI":"10.3390\/electronics11244137","volume":"11","author":"J Peta","year":"2022","unstructured":"Peta, J., Koppu, S.: An IoT-based framework and ensemble optimized deep maxout network model for breast cancer classification. Electronics 11(24), 4137 (2022)","journal-title":"Electronics"},{"issue":"1","key":"1188_CR31","doi-asserted-by":"publisher","first-page":"17024","DOI":"10.1038\/s41598-022-20653-2","volume":"12","author":"Y Jung","year":"2022","unstructured":"Jung, Y., Kim, T., Han, M.R., Kim, S., Lee, S., Choi, Y.J.: Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder. Sci. Rep. 12(1), 17024 (2022)","journal-title":"Sci. Rep."},{"issue":"1","key":"1188_CR32","first-page":"84","volume":"18","author":"K Senthil","year":"2022","unstructured":"Senthil, K.: Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture. Bio Algorithms Med Syst. 18(1), 84\u201393 (2022)","journal-title":"Bio-Algorithms Med-Systems"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-026-01188-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01188-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-026-01188-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T14:28:35Z","timestamp":1772461715000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-026-01188-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,2]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1188"],"URL":"https:\/\/doi.org\/10.1007\/s44196-026-01188-0","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,2]]},"assertion":[{"value":"7 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study did not involve human participants or animals, and therefore, ethics approval and informed consent were not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval and Consent to Participate"}},{"value":"All authors have reviewed and approved the final manuscript.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"102"}}