Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Conference Papers Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis Subject The topic of the resource Deep learning models; Diagnostic accuracy; Machine learning; Multi-modal data; Ovarian cancer Description An account of the resource This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE. Creator An entity primarily responsible for making the resource George J.; Upreti K.; Poonia R.C.; Alapatt B.P. Source A related resource from which the described resource is derived Proceedings - 2024 3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024, pp. 28-33. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2024-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ICCMSO61761.2024.00023" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ICCMSO61761.2024.00023</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203700273&amp;doi=10.1109%2FICCMSO61761.2024.00023&amp;partnerID=40&amp;md5=cb160274164e5f04a32c2a7f0d8edb4d" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203700273&amp;doi=10.1109%2fICCMSO61761.2024.00023&amp;partnerID=40&amp;md5=cb160274164e5f04a32c2a7f0d8edb4d</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 979-835036139-1 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Conference paper Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant George J., Christ University, Department of Computer Science, Delhi-NCR, Ghaziabad, India; Upreti K., Christ University, Department of Computer Science, Delhi-NCR, Ghaziabad, India; Poonia R.C., Christ University, Department of Computer Science, Delhi-NCR, Ghaziabad, India; Alapatt B.P., Christ University, Department of Computer Science, Delhi-NCR, Ghaziabad, India