Early Stage Detection of Breast Cancer using Mammography screeing to help the Radiologists using Deep Learning
Sushopti D. Gawade, Nikita Pallerla, Sadichchha Ahinave, Advait Nair, Gokul Nair · 2024
The uncontrolled proliferation of breast cancer cells in a specific area of the body is the second most common cause of death among women worldwide. If the disease is detected in its early stages, it can be cured. While the use of digital image processing to detect breast cancer is not new, many new approaches are being developed to accurately predict its location. The current approach involves both visual inspection of the tumor region and determining the region in which most of the tumor is concentrated. The primary objective of this study is to determine the most efficient algorithm or combination of algorithms for the detection of breast tumors. Various algorithms have been employed in the proposed work; however, the CNN-Deep Learning model combination is the most effective for the diagnosis of breast cancer. This approach may help radiologists to evaluate screening mammography more effectively.