Early detection of breast cancer is clinically important to reduce the mortality rate. In this study, a new computer-aided detection (CAD) and classification system is introduced to classify two types of mammogram tumors (i.e., mass and calcification) as either benign or malignant. In this CAD system, the tumor-like regions (TLRs) are identified using the automated optimal Otsu thresholding method. Afterward, deep convolutional neural networks (CNNs) process the extracted TLRs to extract relevant mammogram features, investigating AlexNet and ResNet-50 architectures. The normalized extracted CNN features are further input to a support vector machine classifier to decode the classes of mammogram structures (i.e., Benign Calcification, Benign Mass, Malignant Calcification, and Malignant Mass nodules). The experimental results are tested on 2800 mammogram images from the Curated Breast Imaging Subset of Digital Database of Screening Mammography, a publicly available dataset. The accuracy of the proposed CAD system, to classify the ROI into one of the four classes, achieves 0.91 using AlexNet and 0.84 using ResNet-50 models, using fivefold cross-validation. Comparison results with the related methods confirm the advantages of the proposed CAD system.

早期发现乳腺癌对降低死亡率具有重要的临床意义。在这项研究中，引入了一种新的计算机辅助检测（CAD）和分类系统，以将两种类型的乳房X线照片肿瘤（即肿块和钙化）分类为良性或恶性。在此CAD系统中，使用自动化的最佳Otsu阈值化方法来识别肿瘤样区域（TLR）。之后，深度卷积神经网络（CNN）处理提取的TLR，以提取相关的乳房X线照片特征，并研究AlexNet和ResNet-50架构。将标准化提取的CNN特征进一步输入到支持向量机分类器，以解码乳房X线照片的结构类别（即，良性钙化，良性肿块，恶性钙化和恶性肿块）。实验结果在来自筛查乳腺X射线摄影术数字数据库（可公开获得的数据集）的固化乳房成像子集的2800幅乳腺X射线照片上进行测试。拟议的CAD系统将ROI分为四个类别之一的准确性，使用AlexNet并使用五重交叉验证，使用AlexNet可以达到0.91，而使用ResNet-50模型则可以达到0.84。与相关方法的比较结果证实了所提出的CAD系统的优势。