## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT and CXR Images Using Multimodal Deep Learning Models  
Models based on data from diverse radiological imaging modalities can achieve a higher accuracy rate compared to models that use only one imaging modality. However, analyzing CXR and CT images for the same patient in the context of the COVID-19 pandemic is impractical in clinical practice and is considered a significant gap in prior studies. [PERSON] et al. [70] proposed multimodal imaging data to detect COVID-19 by combining CXR, ultrasound, and CT scans using a VGG-19 model. A publicly accessible dataset gathered from different data sources, containing 1118 COVID-19 images, 996 pneumonia images, and 60,533 images with no findings, was used in the experiment. To prevent bias in classification, the original dataset was randomly distributed into 20% for validation and 80% for training. To enhance different features in the original images, the contrastive limited adaptive histogram equalization (CLAHE) method was used. Since the different deep learning models have different limitations in terms of image size, the original image dataset had to be modified due to the utilization of different deep learning models, and the input image sized varied from 224 \(\times\) 224 pixels for VGG variants to 299 \(\times\) 299 pixels for Inception-v3. The performance of the proposed approach varied based on the type of data modality being used for system training, such that the highest performance was provided by the proposed models trained on ultrasound data and the lowest performance was provided for the proposed models trained on CT scan data. The results indicate that ultrasound images were more accurate compared to CXR and CT scans, with a precision of 100% compared to 86% and 84%, respectively. [PERSON] et al. [71] demonstrated the possibility of integrating CXR images with CT scans in deep learning models to diagnose COVID-19 patients. A deep COVIX-Net model was used to classify CXR and CT images into one of the following three classes: normal, COVID-19, and pneumonia. The dataset was obtained from the Kaggle and GitHub repositories, consisting of 9000 CXR images (3000 pneumonia, 3000 COVID-19, and 3000 normal) and 6000 CT scans (3000 pneumonia and 3000 COVID-19). The original dataset was randomly distributed between 80% training and 20% validation subsets; this ensured that the developed system was able to minimize the classification bias. The images from the original dataset were converted to 224 \(\times\) 224 pixels, and these images were subjected to different techniques for feature extraction. The features were extracted from the medical images using the following techniques: texture, gray-level co-occurrence matrix (GLCM), gray-level difference method (GLDM), fast Fourier transform (FFT), and discrete wavelet transform (DWT). From the different feature extraction techniques used, some of the different statistical features used for model training included average, skewness, kurtosis, energy, average deviation, dimension, RMS, consistency, average gradient, minimum, and median. Unfortunately, there is no further information regarding the structure of deep COVIX-Net shown in the paper. The outputs from feature extraction from different techniques were given as inputs to a random forest classifier to provide the final classification output. The proposed model achieved promising outcomes, with 96.8% accuracy for the CXR images and 97% for CT scans. [PERSON] et al. [72] proposed a deep unsupervised framework (Lung-GANs) to classify lung diseases based on chest CT scans and CXR images using unlabeled data. The proposed method involved the use of six large, publicly available datasets consisting of 38,155 CXR and CT scan images from healthy, sick, tuberculosis (TB), viral pneumonia, COVID-19, and non-COVID-19 classes.