Breast Cancer: Using Deep Transfer Learning Techniques AlexNet Convolutional Neural Network For Breast Tumor Detection in Mammography Images
Saida Sarra Boudouh, Mustapha Bouakkaz
Abstract
Breast cancer is the most common form of cancer among women worldwide. Mammography has become a valuable tool for detecting breast cancer. We were able to achieve the purpose of our study, which was to create an accurate Convolutional Neural Network (CNN) model that classifies mammography images into normal and abnormal using deep transfer learning and data augmentation approaches, to avoid overfitting issues with images. The Mammographic Image Analysis Society MiniMammographic Database (MiniMIAS) was used to train and test the CNN model. The shortage of abnormal images in the MiniMIAS caused a low accuracy. Because of that, 92 abnormal images were added from the Digital Database for Screening Mammography (DDSM), which leads us to great accuracy. The proposed method starts with a pre-processing step which includes several filters that eliminate any noises, background, enhances the images, and data augmentation for better training. AlexNet was modified and trained after splitting the dataset into 75%, 5%, and 20% as training, validation, and testing sets respectively. The evaluation results were not very satisfying with the MiniMIAS database with 96.87%. On the other hand with balanced data, the obtained results were very satisfying with 99.99%, and better results according to the existing models, which prove that the chosen filters in the pre-processing phase, as well as the chosen pre-trained model AlexNet, is very useful and suitable for breast tumor detection.