Precision-Driven Classification of Mammographic Images for Early Detection of Breast Cancer
DOI:
https://doi.org/10.48047/Keywords:
.Abstract
Breast cancer remains one of the most common and life-threatening diseases among women worldwide. Early detection is critical for improving survival rates, and mammographic imaging is the most widely used screening tool for breast cancer diagnosis. However, current methods for interpreting mammograms rely heavily on manual analysis by radiologists, which can be time-consuming,
subjective, and prone to variability. Although computer-aided diagnosis (CAD) systems are available, they often lack the accuracy and consistency needed for reliable clinical application.
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References
Meenalochini, G., and S. Ramkumar. "Survey of machine learning algorithms for breast cancer detection using mammogram images." Materials Today: Proceedings 37 (2021): 2738-2743.
Darweesh, M. Saeed, Mostafa Adel, Ahmed Anwar, Omar Farag, Ahmed Kotb, Mohamed Adel, Ayman Tawfik, and Hassan Mostafa. "Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images." Cogent Engineering 8, no. 1
(2021): 1968324.
Alshammari, Maha M., Afnan Almuhanna, and Jamal Alhiyafi. "Mammography image-based diagnosis of breast cancer using machine learning: a pilot study." Sensors 22, no. 1 (2021): 203
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