As a result, PNN was observed to give better results for both criteria. Ucar et al. [17] adapted SqueezeNet for the diagnosis of COVID-19 by combining with BO. The BO method was used for the optimization of HP. The proposed method classified three classes of X-ray images labeled Normal...
We then use each fold as a validation set and a test set, respectively, as shown in Fig. 5. We perform 30 experiments on each fold and take the average of the results as the final results. We set the initial learning rate as 10−4. When the validation accuracy does not grow three...
Classification results of the proposed STM-RENet with and without Channel Boosting on the test set of CoV-Healthy-6k are illustrated in Table 1. The discrimination ability in terms of accuracy (STM-RENet: 97.98%, CB-STM-RENet: 98.53%), F-score (STM-RENet: 0.98, CB-STM-RENet: 0.98)...
To train the model, we utilize a large dataset of publicly accessible chest radiograph images, which includes clinical results consistent with COVID-19, along with verified cases of COVID-19 for creating the test dataset [8]. By leveraging the power of CNN architecture, our DL model can ...
test was performed using a kit from a different manufacturer and the results were also positive for all patients. The patients continued to be asymptomatic by clinician examination and chest CT findings showed no change from previous images. They did not report contact with any person with ...
Figure 6 displays the test accuracy for different resolutions and training steps with and without mixup. The results emphasize that a higher resolution can increase accuracy in identification, which means that clearer CT images contain more diagnostic clinical information. A larger training step can ...
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Furthermore, the ingenuity of the results have been established by performing Wilcoxon rank test with 95% level of significance. Similar content being viewed by others Classification of COVID-19 on Chest X-Ray Images Using Deep Learning Model with Histogram Equalization and Lung Segmentation ...
We also test the opposite scenario, in which we use all images from the COVID-CT dataset [15] for training and all images of SARS-CoV-2 CT-scan dataset [14] to test. Table 7. Cross-dataset results. Training datasetTest datasetAcc (%)SeC (%)+PC (%) SARS-CoV-2 CT-scan dataset ...
(CNN) model. They report promising results on binary classification of CXR images into COVID-19 vs Non-COVID with an accuracy of 98.91 % and a low false negative rate comparing to standalone CNN model. Similarly, Das et al.52proposed an ensemble of CNN models, namely, DenseNet2139, Res...