Breast Ultrasound Images Dataset (Dataset BUSI)

@article{,
title= {Breast Ultrasound Images Dataset (Dataset BUSI)},
keywords= {},
author= {},
abstract= {The data collected at baseline include breast ultrasound images among women in ages between 25 and 75 years old. This data was collected in 2018. The number of patients is 600 female patients. The dataset consists of 780 images with an average image size of 500*500 pixels. The images are in PNG format. The ground truth images are presented with original images. The images are categorized into three classes, which are normal, benign, and malignant.


If you use this dataset, please cite:
Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.


| Subject area               | Medicine and Dentistry                                                                                                                                                             |
|----------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| More specific subject area | Radiology and Imaging                                                                                                                                                              |
| Type of data               | Images and mask images                                                                                                                                                             |
| How data was acquired      | LOGIQ E9 ultrasound and LOGIQ E9 Agile ultrasound system                                                                                                                           |
| Data format                | PNG                                                                                                                                                                                |
| Experimental factors       | All images are classified as normal, benign and malignant                                                                                                                          |
| Experimental features      | When medical images are used for training deep learning models, they provide fast and accurate results in classification, detection, and segmentation of breast cancer.            |
| Data source location       | Baheya Hospital for Early Detection & Treatment of Women's Cancer, Cairo, Egypt.                                                                                                   |
| Data accessibility         | https://scholar.cu.edu.eg/?q=afahmy/pages/dataset                                                                                                                                  |
| Related research article   | 1. Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled and Aly Fahmy, Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images [1] |



https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906728/


https://i.imgur.com/WV1Tfb7.png},
terms= {},
license= {},
superseded= {},
url= {https://scholar.cu.edu.eg/?q=afahmy/pages/dataset}
}