Ultrasound Radio Frequency Time Series for Tissue Typing
This research introduces an innovative ML-based approach to differentiate between diverse breast lesion types using in vivo US RF time series data. The findings underscore its efficacy in enhancing classification accuracy, promising significant strides in computer-aided diagnosis (CAD) for BC screening.
Authors
- Ali Fallah (ORCID: https://orcid.org/0000-0001-5078-8391)
- Mahsa Arab (ORCID: https://orcid.org/0000-0003-3569-2740)
- Saeid Rashidi (ORCID: https://orcid.org/0000-0003-1075-989X)
- Maryam Mehdizadeh Dastjerdi (ORCID: https://orcid.org/0000-0001-7277-2585)
- Nasrin Ahmadinejad (ORCID: https://orcid.org/0000-0003-0444-2538)
Institutions
- Islamic Azad University, Tehran (IR)
- Amirkabir University of Technology (IR)
- Imam Khomeini Hospital (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- Journal of Ultrasound in Medicine
- Published
- 2025-06-30
- DOI
- https://doi.org/10.1002/jum.16745
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00