WAVELET-POOLING-BASED MULTI-SCALE CNN FOR COVID-19 DETECTION FROM CT IMAGES

COVID-19 is a respiratory disease affecting humans and animals. The disease has rapidly spread worldwide and became a pandemic in 2020. Preventing the virus from spreading has become increasingly challenging, especially with the need to test potential suspects rapidly. Deep learning-based methods have been developed to address this challenge of detecting COVID-19 from chest Computed Tomography (CT) images. The proposed network has multi-scale feature extraction layers with wavelet pooling. Learning features at different scales will enable the architecture to explore local patterns at different dimensions. So, in the proposed architecture, we have included a multi-scale convolutional layer to focus on sparse local regions about the disease conditions. Texture-based feature learning using wavelet pooling is incorporated into the architecture to improve detection performance. The proposed network achieved an accuracy of 99.79% with an AUC value of 0.9999. Compared with the existing methods, the proposed network has a lower computational cost regarding learnable parameters, FLOPS, and memory requirements. The proposed CNN model benefits from multi-scale structure and wavelet-pooling, resulting in superior performance compared to previous algorithms.