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Multi-scale CNN based on region proposals for efficient breast abnormality recognition

机译：基于区域提议的多尺度CNN，可有效识别乳房异常

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Mammographic pattern recognition is one of the most essential tasks in breast cancer diagnosis, and has been studied for several years now to make it suitable and faster. In this paper, we developed a novel deep Convolutional Neural Network (CNN) approach to discriminate normal from abnormal breast tissues using Gaussian pyramid representation for multi-scale analysis (Pyramid-CNN). In order to improve image processing time, we extracted representative region proposals from each mammogram using determinant of the Hessian operator. To improve performance of our model and avoid overfitting, data augmentation techniques based on geometric transformation and sub-histogram equalization were applied on all regions to increase the variance of significant mammographic samples. We evaluated our methodology on the publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. In comparison with the current state-of-the-art methods, the experiments show that our proposed system provides efficient results, achieving the average accuracy of 96.84%, sensitivity of 92.12%, specificity of 98.02%, precision of 92.15%, F1-score of 92.12%, and area under the receiver operating characteristic curve (AUC) of 96.76%. Hence, the study demonstrates that our proposed approach has the potential to significantly improve the conventional recognition and classification strategies for use in advanced clinical application and practice or in general, biomedical imaging field.
机译：乳房X线照片模式识别是乳腺癌诊断中最重要的任务之一，并且已经对其进行了数年研究，以使其更加合适和快捷。在本文中，我们开发了一种新颖的深层卷积神经网络（CNN）方法，使用高斯金字塔表示法对正常乳房和异常乳腺组织进行区分，以进行多尺度分析（Pyramid-CNN）。为了缩短图像处理时间，我们使用Hessian算子的行列式从每个乳房X线照片中提取了代表性区域建​​议。为了提高模型的性能并避免过度拟合，在所有区域上应用了基于几何变换和亚直方图均衡化的数据增强技术，以增加重要的乳腺摄影样本的方差。我们在可公开获得的乳腺X射线摄影数据集（如乳腺癌数字资料库（BCDR）数据库）上评估了我们的方法。与目前的最新方法相比，实验表明，我们提出的系统可提供有效的结果，平均准确度为96.84％，灵敏度为92.12％，特异性为98.02％，精度为92.15％，F1-得分为92.12％，接收器工作特性曲线下面积（AUC）为96.76％。因此，研究表明，我们提出的方法有可能显着改善用于高级临床应用和实践或一般生物医学成像领域的常规识别和分类策略。

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