Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification
- Title
- Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification
- Creator
- Mukku L.; Thomas J.
- Description
- It is essential to enhance the accuracy of automatic cervical cancer diagnosis by combining multiple forms of information obtained from a patients primary examination. However, existing multimodal systems are not very effective in detecting correlations between different types of data, leading to low sensitivity but high specificity. This study introduces a deep learning system for automatic diagnosis of cervical cancer by incorporating multiple sources of data. First, a convolutional neural network (CNN) to transform the image database to a vector that can be combined with non-image datasets is used. Subsequently, an investigation of jointly the nonlinear connections between all image and non-image data in a deep neural network is performed. Proposed deep learning-based method creates a unified system that takes advantage of both image and non-image data. It achieves an impressive 89.32% sensitivity at 91.6% specificity when diagnosing cervical intraepithelial neoplasia on a wide-ranging dataset. This result is far superior to any single-source system or prior multimodal approaches. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-865, pp. 109-118.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Cervical cancer; Deep learning; Early fusion
- Coverage
- Mukku L., CHRIST (Deemed to be University), Kengeri, 560074, India; Thomas J., CHRIST (Deemed to be University), Kengeri, 560074, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-981999042-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mukku L.; Thomas J., “Multimodal Early Fusion Strategy Based on Deep Learning Methods for Cervical Cancer Identification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19517.