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

机译：基于深度学习分类，使用当地二元图案（LBP）自动乳腺肿瘤诊断

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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 %.
机译：乳腺癌是全世界癌症死亡的第五个最常见的癌症死亡原因，即使在每年为约12,000名新案件的阿尔及利亚也是如此。纹理描述一直是对模式识别的模式识别方法，以便深入了解特色图像，在本文中，我们研究了本地二进制模式纹理和自动乳房肿瘤图像分类的深度学习方法的能力，是计算机辅助的有效元素诊断（CAD）系统，其中来自输入图像的有意义信息的提取不需要特征提取器。在我们通过标准CNN基于原点图像作为输入将其分类结果与标准CNN进行比较，我们提出了基于LBP图像的卷积神经网络（CNN）架构作为输入。来自（DDSM）数据库的190分段图像将用于测试所提出的方法。分类（良性或恶性肿瘤）的实验结果比标准的CNN方法更好，总精度约为96.32％。

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