Name | DL | Torrents | Total Size | Joe's Recommended Mirror List [edit] | 211 | 6.39TB | 1823 | 0 | Computer Vision [edit] | 73 | 1.34TB | 551 | 0 | Medical [edit] | 76 | 2.00TB | 691 | 0 | ultrasound [edit] | 1 | 205.87MB | 14 | 0 |
![]() | 205.87MB |
Type: Dataset
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Bibtex:
Tags:
Bibtex:
@article{, title= {Breast Ultrasound Images Dataset (Dataset BUSI)}, keywords= {}, author= {}, abstract= {https://i.imgur.com/WV1Tfb7.png | 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Masses using Ultrasound Images [1] | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6906728/}, terms= {}, license= {}, superseded= {}, url= {https://scholar.cu.edu.eg/?q=afahmy/pages/dataset} }