The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority. Such automatic systems are usually based on traditional machine learning or deep learning methods. Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images. We argue that the uncertainty of the model's predictions should be taken into account in the learning process, even though most of existing studies have overlooked it. We quantify the prediction uncertainty in our feature fusion model using effective Ensemble MC Dropout (EMCD) technique. A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves. The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08\% and 96.35\% for the considered CT scan and X-ray datasets, respectively. Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data. The source code of our implementation is freely available at: https://github.com/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification.
翻译:COVID-19(Corona病毒病 2019年)大流行已成为对人类健康和福祉的重大全球威胁,因此,开发计算机辅助检测系统(CAD)是当前的优先事项,该系统能够精确地将COVID-19与其他疾病区别开来,使用胸部计算断层成像(CT)和X射线数据。这种自动系统通常基于传统的机器学习或深层学习方法。与大多数现有研究不同,这些研究在COVID-19案例分类中使用CT扫描或X射线图像,我们展示了一种简单而高效的深层次学习特征聚合模型,称为 " 不确定性FuseNet ",它能够精确地对这两种类型的图像的大型数据集进行分类。我们认为,在学习过程中,即使大多数现有研究都忽视了模型的不确定性或深层学习方法。我们用有效的Ensmlasble MC 丢弃(EMCD) 技术来量化了我们特征混合模型的预测不确定性。我们进行了全面的模拟研究,将我们的新模型的结果与现有方法进行了比较,评估了我们目前对精确性模型的准确性模型的准确性,并且提供了我们的精确性数据。