## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in CXR Images Using Deep Learning Models
The choice of the models was due to their architectures, which contain a small number of layers, thereby reducing the processing time, the memory consumed, and the proposed model's complexity. The highest test accuracy (80.6%) for a scenario that included all four classes was achieved by the GoogLeNet framework. [PERSON] et al. [53] proposed a batch-based CNN approach with a probabilistic Grad-CAM saliency map that was compatible with a batch-based approach. This approach considered the limited availability of CXR images for the classification of COVID-19. The researchers used a public dataset containing 502 CXR images, including 180 COVID-19 images, 191 normal images and 113 belonging to three other classes: viral pneumonia, bacterial pneumonia, and tuberculosis. To reduce classification bias in the system, the data were randomly distributed into training (354 images), validation (49 images), and test (99 images) sets. The CXR images were first preprocessed for data normalization, and the images were resized to 224 \(\times\) 224 pixels to obtain the preprocessed data. The preprocessed data were then fed into a segmentation network, from which lung areas could be extracted for network training and classification using patch-by-patch training and inference. The final decision regarding the network classification was based on majority voting. The experimental results were stable with a small dataset and achieved 88.9% accuracy using the proposedbatch-based CNN approach. The effects of patch size, different segmentation methods (e.g., U-Net, FC-DenseNet63, FC-DenseNet-103), and training dataset size were also evaluated in relation to the overall performance of the proposed system. An attention-based VGG-16 model was used for the classification of COVID-19 in [54]. A total of 4901 image data were used in this study from three different datasets with their own sets of unique challenges and limitations. The original image size was reduced in dimensions to 224 \(\times\) 224 pixels. To reduce the classification bias, data from each dataset were randomly divided into 70% training and 30% validation sets. The proposed attention-based method was based on four main building blocks: an attention module, a convolution module, FC layers, and a softmax classifier. The attention module was used to capture the spatial relationships of visual clues in the COVID-19 CXR images. The output from the attention module was given as an input to both maximum pooling and average pooling on the input tensor, which was the fourth pooling layer of the VGG-16 model in the proposed method. After that, these two resultant tensors (maximum-pooled 2D tensor and average-pooled 2D tensor) were concatenated to one another to perform a convolution, followed by the fully connected layers and the softmax classifier to give the final output. Based on the inherent characteristics and limitations of each dataset, the performance of the proposed approach varied, with the accuracy ranging between 80% and 87%. A deep learning pipeline for the diagnosis and discrimination of viral, non-viral, and COVID-19 pneumonia CXR images was developed in [55]. The dataset used in this study included data from two public datasets: CheXpert and CC-CXRI. The total CXR images included 1571 COVID-19 images, 5656 viral pneumonia images, 11,591 other pneumonia images, and 10,477 normal images. The CXR images were resized to 512 \(\times\) 512 pixels. The common thoracic disease detection module classified the standardized CXR images into 14 different classes. Multiple external validations were performed, with an average ratio of random training, validation, and testing data distribution amounting to 80%, 10%, and 10%, respectively. The following three modules provide the main functionality of the proposed deep learning pipeline: (1) a CXR standardization module, (2) a common thoracic disease detection module, and (3) a final pneumonia analysis module.