Automated Breast Tumor Diagnosis Using Local Binary Patterns (LBP) Based on Deep Learning Classification

Breast cancer is the fifth most common cause of cancer death among women worldwide, even on Algeria that known about 12,000 new cases every year. Texture description has been a great interest in pattern recognition methods for looking deeper into features images, In this paper, we investigate the capability of the Local Binary Pattern texture and deep learning method for automated breast tumor images classification to be an efficient element for Computer aided diagnosis (CAD) system, where the extraction of meaningful information from the input image do not require features extractors. We have proposed a Convolution Neural Network (CNN) architecture based on LBP images as input after we compared their classification results by a standard CNN based on origin images as input. A 190-segmented image from (DDSM) database will be used for testing the proposed approach. Experimental results of the classification (benign or malignant tumor) gave better results than the standard CNN approach with an overall accuracy about 96.32 %.