COVID-19 is a growing issue in society and there is a need for resources to manage the disease. This paper looks at studying the effect of class decomposition in our previously proposed deep convolutional neural network, called DeTraC (Decompose, Transfer and Compose). DeTraC can robustly detect and predict COVID-19 from chest X-ray images. The experimental results showed that changing the number of clusters (decomposition granularity) affected the performance of DeTraC and influenced the accuracy of the model. As the number of clusters increased, the accuracy decreased for the shallow tuning mode but increased for the deep tuning mode. This shows the importance of using suitable hyperparameter settings to get the best results from the DeTraC deep learning model. The highest accuracy obtained, in this study, was 98.33% from the deep tuning model. © ECMS Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela (Editors)
On the effect of decomposition granularity on DeTraC for COVID-19 detection using chest X-ray images