| Subject area | Medicine and Dentistry |
|---|---|
| More specific subject area | Radiology and Imaging放射学和影像学 |
| Type of data | Images and mask images |
| How data was acquired | LOGIQ E9 ultrasound and LOGIQ E9 Agile ultrasound system LOGIQ E9 超声和 LOGIQ E9 Agile 超声系统 |
| Data format | PNG |
| Experimental factors | All images are classified as normal, benign and malignant 所有图像均分为正常、良性和恶性 |
| 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/
磁力下载：magnet:?xt=urn:btih:D0B7B7AE40610BBEAEA385AEB51658F527C86A16
| Info hash | d0b7b7ae40610bbeaea385aeb51658f527c86a16 |
| 文件大小 | 205.87MB (205,873,341 bytes) |
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