论文标题
从具有深度学习方法和与肺部疾病有关的图像数据的X射线图像中提取可能代表性的COVID-19生物标志物
Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases
论文作者
论文摘要
在这项研究中，考虑了从X射线图像中自动分类肺部疾病的问题，包括最近出现的Covid-19。虽然Covid-19的传播增加了，但用于准确检测的新，自动和可靠的方法对于减少医疗专家暴露于暴发至关重要。 X射线成像虽然仅限于特定的可视化，但可能有助于诊断。事实证明，深度学习是从医学图像中提取大量高维特征的一种了不起的方法。具体而言，在本文中，采用并从头开始使用并培训称为移动网的最先进的卷积神经网络，以研究提取功能对分类任务的重要性。 3905个X射线图像的大规模数据集用于训练Mobilenet V2，已被证明可以在相关任务中取得显着的结果。结果表明，从头开始的训练CNN可能会发现与199个疾病相关但不限于COVID疾病的重要生物标志物，而七个类别的总体分类准确性达到87.66％。此外，此方法在检测Covid-19的Covid-19中达到了99.18％的精度，敏感性97.36％和99.42％的特异性。
In this study, the problem of automatically classifying pulmonary diseases, including the recently emerged COVID-19, from X-Ray images, is considered. While the spread of COVID-19 is increased, new, automatic, and reliable methods for accurate detection are essential to reduce the exposure of the medical experts to the outbreak. X-ray imaging, although limited to specific visualizations, may be helpful for the diagnosis. Deep Learning has proven to be a remarkable method to extract massive high-dimensional features from medical images. Specifically, in this paper, the state-of-the-art Convolutional Neural Network called Mobile Net is employed and trained from scratch to investigate the importance of the extracted features for the classification task. A large-scale dataset of 3905 X-Ray images, corresponding to 6 diseases is utilized for training MobileNet v2, which has been proven to achieve remarkable results in related tasks. The results suggest that training CNNs from scratch may reveal vital biomarkers related but not limited to the COVID-19 disease, while an overall classification accuracy of the seven classes reaches 87.66%. Besides, this method achieves 99.18% accuracy, 97.36% Sensitivity, and 99.42% Specificity in the detection of COVID-19.
