In this study, which aims at early diagnosis of Covid-19 disease using X-ray images, the deep-learning approach, a state-of-the-art artificial intelligence method, was used, and automatic classification of images was performed using convolutional neural networks (CNN). In the first training-test data set used in the study, there were 230 X-ray images, of which 150 were Covid-19 and 80 were non-Covid-19, while in the second training-test data set there were 476 X-ray images, of which 150 were Covid-19 and 326 were non-Covid-19. Thus, classification results have been provided for two data sets, containing predominantly Covid-19 images and predominantly non-Covid-19 images, respectively. In the study, a 23-layer CNN architecture and a 54-layer CNN architecture were developed. Within the scope of the study, the results were obtained using chest X-ray images directly in the training-test procedures and the sub-band images obtained by applying dual tree complex wavelet transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying local binary pattern (LBP) to the chest X-ray images. Within the scope of the study, four new result generation pipeline algorithms having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in this study, the training sessions were carried out using the k-fold cross validation method. Here the k value was chosen as 23 for the first and second training-test data sets. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy, F-1 score, and area under the receiver operating characteristic curve (AUC) for the first training-test data set were 0,9947, 0,9800, 0,9843, 0,9881 and 0,9990 respectively; while for the second training-test data set, they were 0,9920, 0,9939, 0,9891, 0,9828 and 0,9991; respectively. Within the scope of the study, finally, all the images were combined and the training and testing processes were repeated for a total of 556 X-ray images comprising 150 Covid-19 images and 406 non-Covid-19 images, by applying 2-fold cross. In this context, the average highest values of sensitivity, specificity, accuracy, F-1 score, and AUC for this last training-test data set were found to be 0,9760, 1,0000, 0,9906, 0,9823 and 0,9997; respectively.

在这项旨在使用 X 射线图像对 Covid-19 疾病进行早期诊断的研究中，使用了最先进的人工智能方法深度学习方法，并使用卷积神经网络对图像进行了自动分类。神经网络（CNN）。在研究中使用的第一个训练测试数据集中，有 230 张 X 射线图像，其中 150 个是 Covid-19，80 个是非 Covid-19，而在第二个训练测试数据集中有 476 个 X - 射线图像，其中 150 个是 Covid-19，326 个是非 Covid-19。因此，已经为两个数据集提供了分类结果，分别主要包含 Covid-19 图像和主要非 Covid-19 图像。在研究中，开发了 23 层 CNN 架构和 54 层 CNN 架构。在研究范围内，结果是在训练-测试过程中直接使用胸部 X 射线图像和通过对上述图像应用双树复小波变换 (DT-CWT) 获得的子带图像获得的。使用通过将局部二值模式 (LBP) 应用于胸部 X 射线图像获得的图像重复相同的实验。在研究范围内，另外提出了四种新的结果生成流水线算法，保证了实验结果的结合，提高了研究的成功率。在本研究进行的实验中，训练课程是使用 k 折交叉验证方法进行的。在这里，第一个和第二个训练测试数据集的 k 值被选为 23。考虑到在研究范围内进行的实验的平均最高结果，第一个训练-测试数据集的灵敏度、特异性、准确性、F-1 评分和接受者操作特征曲线下面积 (AUC) 的值分别为分别为 0,9947、0,9800、0,9843、0,9881 和 0,9990；而对于第二个训练测试数据集，它们分别为 0,9920、0,9939、0,9891、0,9828 和 0,9991；分别。最后，在研究范围内，将所有图像合并，并重复训练和测试过程，总共 556 张 X 射线图像，包括 150 张 Covid-19 图像和 406 张非 Covid-19 图像，通过应用 2-折叠十字。在这种情况下，发现最后一个训练测试数据集的灵敏度、特异性、准确度、F-1 分数和 AUC 的平均最高值为 0,9760、1,0000、0,9906、0、9823 和 0,9997；分别。