## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in CXR Images Using Deep Learning Models
The pneumonia analysis module consists of a lung-lesion segmentation model and a final classification model to estimate the subtype of pneumonia and the severity of COVID-19. The lung-lesion segmentation training was based on 1016 CXR images that were manually segmented into four categories and common lesions to develop a model that could differentiate between COVID-19 and other types of pneumonia. The final classification model was developed based on the DenseNet-121 architecture, which was able to perform lung-lesion segmentation and pneumonia diagnosis. The results showed that the proposed deep learning pipeline was able to predict COVID-19 pneumonia with an AUC of 86.8%, along with a recall of 80.65% and precision of 82.05%. Fusion Module-Hand-Crafted Features-Deep Learning Features (FM-HCF-DLF) is another model for COVID-19 CXR classification given in [56]. The study made use of an imbalanced dataset containing 220 images for COVID-19, 27 for normal lungs, 11 for SARS, and 15 for pneumonia. In the preprocessing part of the system, a 1D Gaussian operator was used for noise removal and image smoothing for the input images, followed by resizing the original images down to 299 \(\times\) 299 pixels. The FM model incorporates the fusion of hand-crafted features with the help of local binary patterns (LBPs) and deep learning. The deep learning (DL) features are computed using the convolutional neural network (CNN)-based Inception-v3 framework, followed by a multilayer perceptron (MLP) to provide the final output classification. The proposed method's performance was compared with that of traditional ML algorithms to highlight the superior performance of the proposed model, which achieved 94.08% accuracy. CVDNet is a deep convolutional neural network (CNN) model to distinguish COVID-19 infection from normal lungs and other pneumonia cases using chest X-ray images, as presented in [57]. The proposed architecture is based on a residual neural network and is constructed by using two parallel levels with different kernel sizes to capture the local and global features of the inputs. This model is trained on a publicly available dataset containing a combination of 219 COVID-19, 1341 normal, and 1345 viral pneumonia CXR images. The images are randomly distributed into 70% for training, 10% for validation, and 20% for testing to reduce classification bias. Some of the preprocessing functions include cropping and resizing the original images to provide input images of 256 \(\times\) 256 pixels in size. Two streams with four parallel residual blocks are used for deep feature extraction, followed by feature concatenation leading to a final residual block, and ending with a fully connected layer and a softmax classifier. The proposed system can provide an accuracy of 96.69%, which is comparable to state-of-the-art methods for the classification of COVID-19. [PERSON] et al. [58] used a ResNet-101-based deep learning model. A total of 10,359 images were used in this study, of which 154 were from COVID-19. Despite the over-whelming imbalance in the dataset used in this study, the authors failed to provide an adequate strategy for mitigating the effects of data imbalance on the overall system performance. The input data were randomly distributed into training (3063 images), validation (1313 images), and test (5828 images) sets to evaluate the performance of the proposed system. The best accuracy provided by the proposed system was 71.9%, which is considerably lower than that achieved in many of the studies discussed in this section. Despite the findings of these studies, there are notable limitations in terms of small sample sizes, the use of too few pneumonia classes, and dependence on one modality. These limitations have implications for the potential use of these research findings in real-world healthcare applications.