## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT and CXR Images Using Multimodal Deep Learning Models
A number of different image preprocessing procedures were leveraged for modifying the original dataset images, such as color mode conversion, image resizing (original images were converted to 512 \(\times\) 512 pixels), and image normalization. After that, the preprocessed images were randomly distributed into training (70%) and validation (30%) sets to reduce classification bias. The generator module in Lung-GAN takes in a 100-dimensional vector as its input and outputs a single 512 \(\times\) 512 image. The discriminator module in Lung-GAN is a CNN architecture that can differentiate between real and synthesized images of 512 \(\times\) 512 pixels in size as inputs, and its output is given as a probability value specifying whether the image is real or not. The authors developed an ensemble of classifiers, with linear support-vector classification (SVC) and random forests serving as the base classifiers, which were combined with predictions from each classifier to produce the final result. The CNN architecture was used for both models. The performance of a GAN-based single framework for all binary classifications on all datasets achieved higher accuracy compared to other state-of-the-art unsupervised models in this area. The breakdown of the performance of Lung-GAN for the different datasets showed diverse results, which varied considerably from one dataset to another. The accuracy values ranged between 94% and 99.5%, with an average accuracy of 97.6%. This shows that the proposed system is sensitive to variations in dataset characteristics (i.e., noise, image dimensions, quality and quantity of data, metadata). [PERSON] et al. [73] designed three artificial intelligence models: sANN ML, using machine learning; pVGG TL, using transfer learning; and pCNN DL, using deep learn-ing. The purpose of each model was to detect COVID-19 from CXR and CT scan images. Each model used the ReLU and E-Tanh activation functions. The public dataset was collected from different research and medical centers, and contained a total of 650 CXR images and 746 CT images divided into positive and negative COVID-19 cases. The training and validation sets contained a total of 625 images for training and 10 images for validation. Despite the considerable data imbalance, the study failed to shed light on the techniques to be used for mitigating the underlying issues. For the different models highlighted above,a VGG-16 network, ANNs with different activation functions, and CNNs with different activation functions were used to provide the backbone for classification. The sANN ML and pVGG TL models achieved high accuracy (100%) in the detection of COVID-19 from CXR images. However, pCNN DL did not perform well, which could be attributed to the small size of the dataset. Notably, each model had a low detection performance for CT scan images compared to CXR images. In addition, the E-Tanh activation function yielded positive results for CXR images. Another study made use of 8879 CXR and 3724 CT images for the training and development of the proposed deep learning model [74]. The authors did not specify the distribution of the original data between training, test, and validation sets. In the preprocessing stage of the proposed model, contrast-limited adaptive histogram equalization (CLAHE) was used for contrast and feature enhancement. The use of different data augmentation strategies, such as random transformations (i.e., rotation, horizontal and vertical translations, zooming, and shearing), ensured that the system could be generalized well to unknown data. All of the data were resized to 512 \(\times\) 512 pixels before being used in the system. In the first stage of the proposed model, an In-ception-v3 deep model was trained for the recognition of COVID-19 using multimodal learning by leveraging data from both modalities, i.e., X-ray and CT scan. The second stage was based on a convolutional neural network architecture for recognizing three types of lung disease.