In this study, an attempt has been made to differentiate Novel Coronavirus-2019 (COVID-19) conditions from healthy subjects in Chest radiographs using a simplified end-to-end Convolutional Neural Network (CNN) model and occlusion sensitivity maps. Early detection and faster automated screening of the COVID-19 patients is essential. For this, the images are considered from publicly available datasets. Significant biomarkers representing critical image features are extracted from CNN by experimentally investigating on cross-validation methods and hyperparameter settings. The performance of the network is evaluated using standard metrics. Perturbation based occlusion sensitivity maps are employed on the features obtained from the classification model to visualise the localization of abnormal areas. Results demonstrate that the simplified CNN model with optimised parameters is able to extract significant features with a sensitivity of 97.35% and F-measure of 96.71% to detect COVID-19 images. The algorithm achieves an Area Under the Curve-Receiver Operating Characteristic score of 99.4% with Matthews correlation coefficient of 0.93. High value of Diagnostic odds ratio is also obtained. Occlusion sensitivity maps provide precise localization of abnormal regions by identifying COVID-19 conditions. As early detection through chest radiographic images are useful for automated screening of the disease, this method appears to be clinically relevant in providing a visual diagnostic solution using a simplified and efficient model.

在本研究中，尝试使用简化的端到端卷积神经网络 (CNN) 模型和遮挡敏感度图，在胸部 X 光照片中区分新型冠状病毒 2019 (COVID-19) 状况与健康受试者。对 COVID-19 患者进行早期检测和更快的自动筛查至关重要。为此，图像是从公开可用的数据集中考虑的。通过对交叉验证方法和超参数设置进行实验研究，从 CNN 中提取代表关键图像特征的重要生物标记。使用标准指标评估网络的性能。基于扰动的遮挡灵敏度图应用于从分类模型获得的特征，以可视化异常区域的定位。结果表明，具有优化参数的简化 CNN 模型能够提取重要特征，检测 COVID-19 图像的灵敏度为 97.35%，F 测量为 96.71%。该算法的曲线下面积-接收者操作特征得分为 99.4%，马修斯相关系数为 0.93。还获得了高诊断比值比。遮挡敏感度图通过识别 COVID-19 状况提供异常区域的精确定位。由于通过胸部放射线图像进行早期检测对于疾病的自动筛查非常有用，因此该方法似乎在临床上具有相关性，可以使用简化且有效的模型提供视觉诊断解决方案。