  Breast cancer is one of the most common cause of deaths among women.
Mammography is a widely used imaging modality that can be used for cancer
detection in its early stages. Deep learning is widely used for the detection
of cancerous masses in the images obtained via mammography. The need to improve
accuracy remains constant due to the sensitive nature of the datasets so we
introduce segmentation and wavelet transform to enhance the important features
in the image scans. Our proposed system aids the radiologist in the screening
phase of cancer detection by using a combination of segmentation and wavelet
transforms as pre-processing augmentation that leads to transfer learning in
neural networks. The proposed system with these pre-processing techniques
significantly increases the accuracy of detection on Mini-MIAS.
