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 Faculty Publications 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/. Creator An entity primarily responsible for making the resource Kumar, Ashish; Sandhiya, B.; Suganya Devi, K.; Garg, Amit; Gupta, Rishi Title A name given to the resource The Smart Detection of Ovarian Cancer in Complex Medical Images Using Deep Learning Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1341 LNNS;pp.101-115 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-96-5126-9_8" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-96-5126-9_8</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105020815908?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105020815908?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kumar A., School of Computer Science and Engineering, Galgotias University, Greater Noida, India; Sandhiya B., Department of Computer Science, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Suganya Devi K., Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Tamil Nadu, Coimbatore, India; Garg A., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India; Gupta R., Department of Computer Science and Engineering, Manipal University Jaipur, Rajasthan, Jaipur, India Description An account of the resource Ovarian cancer is a challenging disease to detect and diagnose, especially in complex medical images where the cancerous lesions may be small and difficult to differentiate from surrounding healthy tissue. The use of deep learning algorithms has shown promising results in computer-aided diagnosis of various cancers. This study aims to develop a smart detection system for ovarian cancer in complex medical images using deep learning techniques. The proposed system will have the ability to accurately and efficiently identify cancerous lesions, leading to earlier detection and improved treatment outcomes. Through the use of advanced computer vision and machine learning methods, the system will be able to learn from a large dataset of medical images and make accurate predictions. This research has the potential to significantly improve the diagnosis and treatment of ovarian cancer, ultimately saving lives. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Artificial intelligence; Complex; Deep learning; Medical images; Ovarian cancer; Smart detection Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981965125-2; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online