TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images
- Title
- TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images
- Creator
- Mukku L.; Thomas J.
- Description
- Cervical cancer ranks as the fourth most prevalent malignancy among women globally. Timely identification and intervention in cases of cervical cancer hold the potential for achieving complete remission and cure. In this study, we built a deep learning model based on self-attention mechanism using transformer architecture to classify the cervix images to help in diagnosis of cervical cancer. We have used techniques like an enhanced multivariate gaussian mixture model optimized with mexican axolotl algorithm for segmenting the colposcope images prior to the Temporal Lesion Convolution Neural Network (TelsNet) classifying the images. TelsNet is a transformer-based neural network that uses temporal convolutional neural networks to identify cancerous regions in colposcope images. Our experiments show that TelsNet achieved an accuracy of 92.7%, with a sensitivity of 73.4% and a specificity of 82.1%. We compared the performance of our model with various state-of-the-art methods, and our results demonstrate that TelsNet outperformed the other methods. The findings have the potential to significantly simplify the process of detecting and accurately classifying cervical cancers at an early stage, leading to improved rates of remission and better overall outcomes for patients globally. 2023, Universitas Ahmad Dahlan. All rights reserved.
- Source
- International Journal of Advances in Intelligent Informatics, Vol-9, No. 3, pp. 502-523.
- Date
- 2023-01-01
- Publisher
- Universitas Ahmad Dahlan
- Subject
- Cervical cancer; Colposcopy; Deep learning; Lesions; Transformer architecture
- Coverage
- Mukku L., CHRIST (Deemed to be University), Kengeri, Bangalore, 560074, India; Thomas J., CHRIST (Deemed to be University), Kengeri, Bangalore, 560074, India
- Rights
- All Open Access; Gold Open Access
- Relation
- ISSN: 24426571
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Mukku L.; Thomas J., “TelsNet: temporal lesion network embedding in a transformer model to detect cervical cancer through colposcope images,” CHRIST (Deemed To Be University) Institutional Repository, accessed March 4, 2025, https://archives.christuniversity.in/items/show/14021.