Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
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DOI：
10.1016/j.cmpb.2018.01.017
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发表时间：
2018-04-01
影响因子：
6.1
通讯作者：
Kim, Tae-Seong
中科院分区：
文献类型：
--
作者：
Al-masni, Mohammed A.;Al-antari, Mugahed A.;Kim, Tae-Seong
背景和目的：乳腺X线照片中肿块的自动检测和分类仍然是一个很大的挑战，并发挥着至关重要的作用，以帮助放射科医生进行准确的诊断。在本文中，我们提出了一种基于区域深度学习技术之一的新型计算机辅助诊断（CAD）系统，即基于ROI的卷积神经网络（CNN），称为You Only Look Once（YOLO）。虽然大多数以前的研究只处理群众的分类，我们提出的基于YOLO的CAD系统可以处理检测和分类同时在一个framework.Methods：建议的CAD系统包含四个主要阶段：预处理的乳房X线照片，利用深度卷积网络的特征提取，质量检测的信心，最后质量分类使用全连接神经网络（FC-NNs）。在这项研究中，我们使用了来自数字数据库的原始600张乳腺X线照片，以及2，400张增强的乳腺X线照片，其中包含肿块及其类型的信息，用于训练和测试我们的CAD。训练YOLO为基础的CAD系统检测群众，然后将其类型分为良性或恶性。结果：我们的结果与五倍交叉验证测试表明，建议的CAD系统检测质量的位置，总体准确率为99.7%。该系统还区分良性和恶性病变的总体准确率为97%.Conclusions：我们提出的系统甚至适用于一些具有挑战性的乳腺癌病例，其中肿块存在于胸肌或致密区域。(C)2018爱思唯尔B. V.保留所有权利。
Background and objective: Automatic detection and classification of the masses in mammograms are still a big challenge and play a crucial role to assist radiologists for accurate diagnosis. In this paper, we propose a novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO). Although most previous studies only deal with classification of masses, our proposed YOLO-based CAD system can handle detection and classification simultaneously in one framework.Methods: The proposed CAD system contains four main stages: preprocessing of mammograms, feature extraction utilizing deep convolutional networks, mass detection with confidence, and finally mass classification using Fully Connected Neural Networks (FC-NNs). In this study, we utilized original 600 mammograms from Digital Database for Screening Mammography (DDSM) and their augmented mammograms of 2,400 with the information of the masses and their types in training and testing our CAD. The trained YOLO-based CAD system detects the masses and then classifies their types into benign or malignant.Results: Our results with five-fold cross validation tests show that the proposed CAD system detects the mass location with an overall accuracy of 99.7%. The system also distinguishes between benign and malignant lesions with an overall accuracy of 97%.Conclusions: Our proposed system even works on some challenging breast cancer cases where the masses exist over the pectoral muscles or dense regions. (C) 2018 Elsevier B.V. All rights reserved.