Analysis of breast region segmentation in thermal images using U-Net deep neural network variants
This research highlights that the original U-Net, when trained with the ADAM optimizer, remains highly effective for breast region segmentation in thermal images. The insights contribute to guiding the selection of suitable deep learning models and optimizers for medical image analysis, with the potential to enhance the efficiency and accuracy of breast cancer diagnosis using thermal imaging.
Authors
- Muktar Hussaini (ORCID: https://orcid.org/0000-0002-4492-0365)
- Mohamed Hadi Habaebi (ORCID: https://orcid.org/0000-0002-2263-0850)
- Rafhanah Shazwani Rosli
- Md. Rafiqul Islam (ORCID: https://orcid.org/0000-0003-0808-2840)
Institutions
- Arab Open University (OM)
- International Islamic College (MY)
- International Islamic University Malaysia (MY)
Publication Details
- Journal
- Frontiers in Bioinformatics
- Published
- 2025-10-10
- DOI
- https://doi.org/10.3389/fbinf.2025.1609004
- Primary Topic
- Infrared Thermography in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- International Islamic University Malaysia