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In response to this challenge, this study proposes a novel hybrid deep learning framework for the early detection and classification of ovarian cancer using ultrasound and MRI imaging, designed for deployment on a secure cloud\u2010based platform. The proposed framework departs from conventional convolutional and attention\u2010driven models by introducing a capsule\u2010based representation learning strategy that explicitly encodes lesion morphology and spatial hierarchies, enabling robust characterization of small and irregular tumor structures. To capture global contextual relationships across imaging regions without reliance on self\u2010attention mechanisms, the framework integrates an attention\u2010free token\u2010mixing architecture, facilitating efficient long\u2010range interaction while maintaining scalability. In addition, a hypergraph\u2010based relational learning module is employed to model higher\u2010order spatial and radiomic relationships among multiple lesion regions simultaneously, providing lesion\u2010centric reasoning that aligns with clinical diagnostic practices. This combination allows the model to effectively distinguish malignant patterns from benign anatomical variations. Beyond binary cancer detection, the framework supports hierarchical classification, separating benign and malignant cases and further categorizing malignant tumors into clinically meaningful subtypes. To enhance fine\u2010grained discrimination, pathology\u2010guided semantic alignment is incorporated using histopathological knowledge as auxiliary supervision, enabling cross\u2010modal knowledge transfer without the need for paired imaging\u2013pathology data. The framework is evaluated on multiple publicly available datasets covering ultrasound, MRI\/CT, and histopathology modalities, demonstrating consistent performance across heterogeneous data sources. To ensure suitability for real\u2010world clinical use, an advanced chaos\u2010based image encryption and secure transmission module is integrated to protect sensitive medical data during cloud\u2010based processing. Experimental results indicate that the proposed framework achieves superior detection and classification performance compared to existing approaches, particularly in early\u2010stage ovarian cancer cases, underscoring its potential as an accurate, interpretable, and clinically deployable decision\u2010support system.<\/jats:p>","DOI":"10.1155\/cplx\/9924424","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T10:32:29Z","timestamp":1774607549000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Deep Learning Framework for Early Detection of Ovarian Cancer Using Ultrasound and MRI Images on a Secure Cloud Platform"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6073-3773","authenticated-orcid":false,"given":"Umesh Kumar","family":"Lilhore","sequence":"first","affiliation":[]},{"given":"Vanusha","family":"D.","sequence":"additional","affiliation":[]},{"given":"Srilatha","family":"Gundapaneni","sequence":"additional","affiliation":[]},{"given":"Anto Lourdu Xavier Raj Arockia","family":"Selvarathinam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3567-6783","authenticated-orcid":false,"given":"Rasmi","family":"A.","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7686-8496","authenticated-orcid":false,"given":"Sarita","family":"Simaiya","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8904-9710","authenticated-orcid":false,"given":"Lidia Gosy","family":"Tekeste","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4338-9867","authenticated-orcid":false,"given":"Ehab Seif","family":"Ghith","sequence":"additional","affiliation":[]},{"given":"Heba G.","family":"Mohamed","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"e_1_2_16_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijgc.2025.104452"},{"key":"e_1_2_16_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44174-025-00328-y"},{"key":"e_1_2_16_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/app16020785"},{"key":"e_1_2_16_4_2","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3567081","article-title":"Predicting Ovarian Cancer With Advanced Machine Learning and Deep Learning Techniques","volume":"13","author":"Dhingra H.","year":"2025","journal-title":"IEEE Access"},{"key":"e_1_2_16_5_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/B978-0-443-33028-5.00009-4","volume-title":"Emerging Trends in Medical Robotics","author":"Maria H.","year":"2026"},{"key":"e_1_2_16_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcpo.2025.100582"},{"key":"e_1_2_16_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics15040406"},{"key":"e_1_2_16_8_2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16020422"},{"key":"e_1_2_16_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105608"},{"key":"e_1_2_16_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-025-10268-x"},{"key":"e_1_2_16_11_2","first-page":"10","article-title":"Transforming Gynecologic Cancer Care Through Artificial Intelligence: A Clinician\u2019s Guide","volume":"18","author":"Polio A.","year":"2026","journal-title":"Clinical Obstetrics and Gynecology"},{"key":"e_1_2_16_12_2","doi-asserted-by":"publisher","DOI":"10.35882\/jeeemi.v8i1.1216"},{"key":"e_1_2_16_13_2","doi-asserted-by":"publisher","DOI":"10.3390\/diagnostics13101703"},{"key":"e_1_2_16_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11135-025-02315-3"},{"key":"e_1_2_16_15_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2024.3434722"},{"key":"e_1_2_16_16_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eclinm.2022.101662"},{"key":"e_1_2_16_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-025-01566-8"},{"key":"e_1_2_16_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-024-10130-6"},{"key":"e_1_2_16_19_2","doi-asserted-by":"publisher","DOI":"10.1002\/cam4.71224"},{"key":"e_1_2_16_20_2","doi-asserted-by":"publisher","DOI":"10.1007\/s44174-025-00377-3"},{"key":"e_1_2_16_21_2","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1664201"},{"key":"e_1_2_16_22_2","unstructured":"ZhaoQ. 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