Project DetailsBreast cancer is among the leading cause of mortality among women in developing as well as under developing countries. The detection and classification of breast cancer in early stages of its development may allow patients to have proper treatment. The work faced the problem of mass detection in mammograms, using CNN architecture. The initial architecture of the project was given to us by former student subject to Dr. Dror Lederman. Weve worked and streamlined this initial architecture.
In this work we had to stabilize the existing model so that it would converge and not diverge. In addition, we had to improve the percentage accuracy of the model. After several different steps in network development and multiple approaches taken, we achieved our goal in the project and indeed we were able to stabilize the model as well as increasing the percentage accuracy in the True Positive measure from 0% to 50%.
