论文标题
基于X射线图像的自动检测和分类肺炎的自动化方法使用深度学习
Automated Methods for Detection and Classification Pneumonia based on X-Ray Images Using Deep Learning
论文作者
论文摘要
最近，全球研究人员，专家和公司正在推出基于深度学习和图像处理的系统，这些系统可以迅速处理数百张X射线和计算机断层扫描（CT）图像，以加速肺炎的诊断，例如SARS，COVID-19，并帮助其遏制。医学图像分析是最有前途的研究领域之一，它为诊断和决定的设施提供了许多疾病，例如MERS，Covid-19。在本文中，我们介绍了最近深度卷积神经网络（DCNN）体系结构的比较，用于肺炎图像的自动二进制分类，基于（VGG16，VGG19，Densenet201，inception_resnet_v2，incept_v3，inception_v3，inception_v3，incemnet50，resnet50，mobilenet_v2和Xection）的罚款调谐版本。提出的工作已使用胸部X射线和CT数据集进行了测试，该数据集包含5856张图像（4273次肺炎和1583个正常）。结果，我们可以得出结论，resnet50，mobilenet_v2和inception_resnet_v2的微调版本表现出高度令人满意的性能，训练和验证精度的提高（超过精度的96％以上）。与CNN不同，Xception，VGG16，VGG19，Inception_V3和Densenet201的性能低（精度超过84％）。
Recently, researchers, specialists, and companies around the world are rolling out deep learning and image processing-based systems that can fastly process hundreds of X-Ray and computed tomography (CT) images to accelerate the diagnosis of pneumonia such as SARS, COVID-19, and aid in its containment. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as MERS, COVID-19. In this paper, we present a comparison of recent Deep Convolutional Neural Network (DCNN) architectures for automatic binary classification of pneumonia images based fined tuned versions of (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, MobileNet_V2 and Xception). The proposed work has been tested using chest X-Ray & CT dataset which contains 5856 images (4273 pneumonia and 1583 normal). As result we can conclude that fine-tuned version of Resnet50, MobileNet_V2 and Inception_Resnet_V2 show highly satisfactory performance with rate of increase in training and validation accuracy (more than 96% of accuracy). Unlike CNN, Xception, VGG16, VGG19, Inception_V3 and DenseNet201 display low performance (more than 84% accuracy).
