Segmentation for mammography classification utilizing deep convolutional neural network
In this study, a modified transformer model is proposed for BC prediction and mammography image classification using segmentation approaches. Data segmentation techniques accurately identify the regions affected by BC. Finally, the proposed transformer model accurately classified benign and malignant breast tissues, which is vital for radiologists to guide future treatment.
Authors
- Mejdl Safran (ORCID: https://orcid.org/0000-0002-7445-7121)
- Tuhin Hossain
- M. F. Mridha
- Dip Kumar Saha (ORCID: https://orcid.org/0000-0002-9772-4130)
- Sultan Alfarhood (ORCID: https://orcid.org/0009-0001-1268-9613)
- Dunren Che (ORCID: https://orcid.org/0000-0002-9906-4295)
Institutions
- American International University-Bangladesh (BD)
- Texas A&M University – Kingsville (US)
- King Saud University (SA)
- Stamford University Bangladesh (BD)
- Jahangirnagar University (BD)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1186/s12880-024-01510-2
- Citations
- 15
- Primary Topic
- Infrared Thermography in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 6.06
Funders
- King Saud University