A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
- Title
- A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
- Creator
- George J.; Upreti K.; Poonia R.C.; Alapatt B.P.
- Description
- 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.
- Source
- Proceedings - 2024 3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024, pp. 28-33.
- Date
- 2024-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Deep learning models; Diagnostic accuracy; Machine learning; Multi-modal data; Ovarian cancer
- Coverage
- 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
- Rights
- Restricted Access
- Relation
- ISBN: 979-835036139-1
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
George J.; Upreti K.; Poonia R.C.; Alapatt B.P., “A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/19248.