Abstract Necessary screenings must be performed to control the spread of the COVID‐19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID‐19. The information obtained by using X‐ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X‐ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two‐stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand‐crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over‐sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto‐encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID‐19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.

中文翻译：

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使用收缩特征从 X 射线和 CT 图像中对冠状病毒 (COVID ‐19) 进行分类

摘要 必须进行必要的筛查，以控制 COVID-19 在日常生活中的传播，并对可疑病例进行初步诊断。病理实验室检测持续时间长，检测结果可疑，导致研究人员将注意力集中在不同的领域。快速准确的诊断对于有效干预 COVID-19 至关重要。通过使用 X 射线和计算机断层扫描 (CT) 图像获得的信息对于进行临床诊断至关重要。因此，旨在通过分析 X 射线和 CT 图像来开发一种用于检测病毒流行病的机器学习方法。在这项研究中，属于六种情况的图像，包括冠状病毒图像，使用两阶段数据增强方法进行分类。由于数据集中的图像数量不足且不平衡，第一阶段使用了浅层图像增强方法。使用手工特征提取方法分析这些图像更方便，因为新创建的数据集仍然不足以训练深度架构。因此，合成少数过采样技术算法是本研究的第二个数据增强步骤。最后，通过使用堆叠自动编码器和主成分分析方法来去除特征向量中的相互关联的特征，从而减小特征向量的大小。根据获得的结果，可以看出所提出的方法具有杠杆性能，特别是在短时间内有效地诊断出 COVID-19。此外，它被认为是未来研究不足和不平衡数据集的灵感来源。