Thesis (Ph.D.) - Cairo University - Faculty of Engineering - Department of Systems and Biomedical Engineering

Breast cancer is one of the most prevalent cancers, and currently many computers aided detection/diagnosis (CAD) systems are being used in clinical use. Whilst recent studies have shown that there is a high positive correlation between high breast density and high breast cancer risk.Thus, breast density classification may aid in breast lesion analysis. With this objective, we proposed a framework of two systems; the first one classifies the mammographic images into four categories of breast densities. Different sets of features (First order gray-level parameters, Gray-Level co-occurrence matrices, Laws' texture energy measurements and Zernike moment features) were investigated along with several classifiers.The results achieved a promising classification accuracy of 93.7%. While the second system classifies lesions using 2Transfer learning3 concept based-on pre-trained Convolutional Neural Networks, through investigating and comparing different hyper-parameters to fine-tune several pre-trained models, to find the optimal model configuration proper for each density category

Issued also as CD