In this work, we propose a deep learning framework for the classification of COVID-19 pneumonia infection from normal chest CT scans. In this regard, a 15-layered convolutional neural network architecture is developed which extracts deep features from the selected image samples – collected from the Radiopeadia. Deep features are collected from two different layers, global average pool and fully connected layers, which are later combined using the max-layer detail (MLD) approach. Subsequently, a Correntropy technique is embedded in the main design to select the most discriminant features from the pool of features. One-class kernel extreme learning machine classifier is utilized for the final classification to achieving an average accuracy of 95.1%, and the sensitivity, specificity & precision rate of 95.1%, 95%, & 94% respectively. To further verify our claims, detailed statistical analyses based on standard error mean (SEM) is also provided, which proves the effectiveness of our proposed prediction design.

中文翻译：

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基于选定深度特征和一类核极限学习机的 COVID-19-肺炎预测

在这项工作中，我们提出了一个深度学习框架，用于从正常胸部 CT 扫描中对 COVID-19 肺炎感染进行分类。在这方面，开发了一个 15 层卷积神经网络架构，它从选定的图像样本中提取深层特征——从 Radiopedia 收集。深度特征是从两个不同的层收集的，全局平均池和完全连接的层，然后使用最大层细节（MLD）方法将它们组合起来。随后，在主要设计中嵌入了相关熵技术，以从特征池中选择最具鉴别力的特征。采用一类核极限学习机分类器进行最终分类，平均准确率达到95.1%，灵敏度、特异度和准确率分别达到95.1%、95%和94%。