Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)

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Ovarian Cancer Prediction Using Deep Learning: A Comprehensive Review

Authors
Shilpa Sharma1, *, Ritu Tandon2
1Research Scholar, Department of Computer Science and Engineering, SAGE University, Indore, India
2Associate Professor, Department of Computer Science and Engineering, SAGE University, Indore, India
*Corresponding author. Email: sharma.shilpa8@gmail.com
Corresponding Author
Shilpa Sharma
Available Online 28 May 2026.
DOI
10.2991/978-94-6239-678-4_18How to use a DOI?
Keywords
CAD; Ovarian Cancer; Deep Learning; Region of Interest; Segmentation
Abstract

This systematic study investigates the constraints impeding the implementation of Computer-Aided Diagnosis (CAD) systems in medical diagnostics, with a specific emphasis on Ovarian Cancer (OC). Our comprehensive analysis of existing literature revealed that a major limitation is the inadequate scale and variety of datasets, which negatively impacts the precision of predictive models. Prior research suffers from a lack of thorough testing on diverse datasets, leading to restricted applicability and reliability. Furthermore, there is a pressing requirement for progress in image segmentation and accurate identification of tumor size to improve the precision of (OC) categorization and early-stage prognosis. It is crucial to address these gaps to enhance the accuracy of diagnosis and, as a result, improve the survival rates of patients with (OC). This review highlights the need for stronger, more varied datasets and improved analytical methods to enhance computer-aided diagnosis procedures in (OC).

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
Series
Advances in Intelligent Systems Research
Publication Date
28 May 2026
ISBN
978-94-6239-678-4
ISSN
1951-6851
DOI
10.2991/978-94-6239-678-4_18How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Shilpa Sharma
AU  - Ritu Tandon
PY  - 2026
DA  - 2026/05/28
TI  - Ovarian Cancer Prediction Using Deep Learning: A Comprehensive Review
BT  - Proceedings of the 2nd International Conference on Recent Advancement and Modernization in Sustainable Intelligent Technologies & Applications (RAMSITA-2026)
PB  - Atlantis Press
SP  - 221
EP  - 231
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-678-4_18
DO  - 10.2991/978-94-6239-678-4_18
ID  - Sharma2026
ER  -