Comparative Study of Deep Learning Based Model Approach for Early Breast Cancer Detection

Authors

  • Bhavna Pancholi, Aasma A Chauhan

Keywords:

Breast Cancer Detection, Mammogram imaging, Deep learning, CNN, VGG16, ResNet50, CBIS- DDSM, Classification

Abstract

In cancer Breast cancer is indeed one of the most common cancers globally and a leading cause of cancer related deaths, though it primarily affects women. It is the most common cancer among women and the second most common overall, after lung cancer. In men, breast cancer is much rarer but still a serious concern, with significantly lower incidence rates compared to women. Early detection and advances in treatment have improved survival rates over the years. Breast cancer represents about 30% of all new cases diagnosed in women each year. According to the American Cancer Society’s 2024 estimates, there will be approximately 310,720 new cases of invasive breast cancer and 56,500cases of ductal carcinoma in situ (DCIS) diagnosed [1]. Early detection is crucial in improving outcomes for breast cancer. Today’s emerged AI and deep learning techniques can also improve both accuracy and the effectiveness of treatment and gives better outputs. This paper represents the comparative study of deep learning-based CNN architectures for breast cancer detection with CBIS-DDSM Mammogram Dataset.

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Published

30.04.2024

How to Cite

Bhavna Pancholi. (2024). Comparative Study of Deep Learning Based Model Approach for Early Breast Cancer Detection . International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 5261 –. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8167

Issue

Vol. 12 No. 21s (2024)

Section

Research Article

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