A Review on Diagnosis of Breast Cancer Using Mammography Techniques
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2100-3_51
Reference28 articles.
1. Swapnil P et al (2016) Region marking and grid based textural analysis for early identification of breast cancer in digital mammography. In: International conference on advanced computing, IEEE
2. Giger (2018) ML: Machine learning in medical imaging. J Am Coll Radiol 15(3 Pt B):512–520
3. Rahmatika A et al (2019) Automated segmentation of breast tissue and pectoral muscle in digital mammography. 978-1-5386-8448-1/19/$31.00, IEEE
4. Salama MS et al (2018) An improved approach for computer-aided diagnosis of breast cancer in digital mammography. 978-1-5386-3392-2/18/$31.00, IEEE
5. Parisa Beham M et al (2020) MAMMSIT: A Database For The diagnosis and detection of Breast Cancer in Mammography images. Authorized licensed use limited to: Cornell University Library. Downloaded on August 27, 2020 at 08:11:26 UTC from IEEE
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