Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be associated with better overall clinical outcomes. Ultrasonography is a vital imaging modality in managing breast lesions. In addition, the development of computer-aided diagnosis (CAD) systems has further enhanced the importance of this imaging modality. Proper development of robust and reproducible CAD systems depends on the inclusion of different data from different populations and centers to considerate all variations in breast cancer pathology and minimize confounding factors. The current database contains ultrasound images and radiologist-defined masks of two sets of histologically proven benign and malignant lesions. Using this and similar pieces of data can aid in the development of robust CAD systems.
If you use this dataset, please cite:
[1] A. Abbasian Ardakani, A. Mohammadi, M. Mirza-Aghazadeh-Attari, U.R. Acharya, An open-access breast lesion ultrasound image database: Applicable in artificial intelligence studies, Computers in Biology and Medicine, 152 (2023) 106438. https://doi.org/10.1016/j.compbiomed.2022.106438
[2] H. Hamyoon, W. Yee Chan, A. Mohammadi, T. Yusuf Kuzan, M. Mirza-Aghazadeh-Attari, W.L. Leong, K. Murzoglu Altintoprak, A. Vijayananthan, K. Rahmat, N. Ab Mumin, S. Sam Leong, S. Ejtehadifar, F. Faeghi, J. Abolghasemi, E.J. Ciaccio, U. Rajendra Acharya, A. Abbasian Ardakani, Artificial intelligence, BI-RADS evaluation and morphometry: A novel combination to diagnose breast cancer using ultrasonography, results from multi-center cohorts, European Journal of Radiology, 157 (2022) 110591. https://doi.org/10.1016/j.ejrad.2022.110591
[3] H. Homayoun, W.Y. Chan, T.Y. Kuzan, W.L. Leong, K.M. Altintoprak, A. Mohammadi, A. Vijayananthan, K. Rahmat, S.S. Leong, M. Mirza-Aghazadeh-Attari, S. Ejtehadifar, F. Faeghi, U.R. Acharya, A.A. Ardakani, Applications of machine-learning algorithms for prediction of benign and malignant breast lesions using ultrasound radiomics signatures: A multi-center study, Biocybernetics and Biomedical Engineering, 42 (2022) 921-933. https://doi.org/10.1016/j.bbe.2022.07.004
Benign
Malignant
