Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification
- Title
- Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification
- Creator
- Mukku L.; Thomas J.
- Description
- Cervical cancer remains a significant burden on public health, particularly in developing countries, where its malignancy and mortality rates are alarmingly high. Early diagnosis stands as a pivotal factor in effectively treating and potentially curing the cervical cancer. This study introduces a novel approach of meta module based on recurrent gate architecture designed to enhance the classification of cervix images efficiently. This innovative framework incorporates a meta module capable of dynamically selecting image modalities most pertinent attributes. Furthermore, it integrates clinical data with extracted image features and employs a range of EfficientNet architectures (B0-B5) for image classification. Our results indicate that the EfficientNet B5 architecture outperforms its counterparts, achieving an AUC (Area Under the Curve) score of 55.1 and an F1-Score of 75.1. Overall, this work represents a crucial step towards improving the early detection of cervical cancer, which in turn can lead to more effective treatment strategies and, ultimately, better outcomes for patients worldwide. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
- Source
- Lecture Notes in Networks and Systems, Vol-1046 LNNS, pp. 286-295.
- Date
- 2024-01-01
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- attention; cervical cancer; Deep learning; EfficientNet; feature integration
- Coverage
- Mukku L., CHRIST (Deemed to be University), Bangalore, India; Thomas J., CHRIST (Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 23673370; ISBN: 978-303164812-0
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Mukku L.; Thomas J., “Attention Based Meta-Module to Integrate Cervigrams with Clinical Data for Cervical Cancer Identification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19278.