Breast Cancer Detection and Classification based on Multilevel Wavelet Transformation

Breast cancer afflicts more than one million women in the world each year. It is the second leading cause of death in cancer patients globally. It is the second most prevailing cancer following lungs cancer. Wavelet transformation and wavelet mathematical functions have proved extremely useful for processing digital signals and digital images in last few decades. In this research, breast cancer digital mammograms are processed through wavelet transforms for extracting useful features which can then be used to train classifier. This work shows that processing digital mammograms with the aid of multilevel wavelet transformation yields much better performance and can help radiologists identify and classify breast cancer with better accuracy.