## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT Images Using Deep Learning Models
Another study proposed a DL-based pipeline for CT images called CoviWavNet for the automatic diagnosis of COVID-19 [62]. In the preprocessing phase of the proposed system, data from two different datasets are concatenated and augmented to increase the size of each of the datasets and reduce overfitting. Some of the augmentation processes performed include scaling, shearing, rotation, flipping on the x- and y-axes, and random translation in the x- and y-directions. Finally, the augmented images are resized to 227 \(\times\) 227 \(\times\) 3 pixels. The proposed CoviWavNet uses multilevel discrete wavelet transform (DWT) and heatmaps of the approximation levels to train three different ResNet models for classification. To examine the effect of the combination of spatial and spectral-temporal information on diagnostic accuracy, deep spectral-temporal features are generated from ResNet using transfer learning and integrated with deep spatial features extracted from ResNet models trained with the original CT slices. To reduce the dimensionality, the most valuable feature is selected using the minimum-redundancy-maximum-relevance (mRMR) technique and used as inputs to three support-vector machine (SVM) classifiers. The performance of the proposed system achieves accuracy of 98.62%, precision of 99.54%, an F1-score of 99.62%, and recall of 99.69%. [PERSON] et al. [63] applied two separate CT datasets for developing a deep-learning-based system for the classification of COVID-19 using the most advanced networks, such as SqueezeNet, Inception, ResNet, ResNeXt, Xception, ShuffleNet, and DenseNet. The original images from the two datasets were randomly distributed into 60% for training and 40% for testing, and were resized to 253 \(\times\) 349 \(\times\) 3 pixels. Data augmentation methods were implemented to effectively increase the number of training samples for improved generalization. Some of these methods included random horizontal flipping, normalization, cropping, blurring, Gaussian noise addition, and brightness and contrast improvement. To assess the performance of the proposed models, stratified K-fold (K = 5) cross-validation was used to ensure class-level consistency in each of the five folds. The proposed model achieved high accuracy, with 99.4%, and an F1-score of 99.4%. Another study proposed a combination of radiomics and artificial intelligence for the analysis of medical images using a CAD framework with four phases [64]: image preprocessing, feature extraction, feature fusion, and classification. In the first phase, the images are analyzed using two texture-based radiomic approaches: gray-level co-variance matrix (GLCM), and discrete wavelet transform (DWT). The radiomics and original CT images are resized to 227 \(\times\) 227 \(\times\) 3 pixels before they are given as inputs for the feature generation and extraction phase. Different data augmentation techniques are performed on the radiomics and original CT data, including shearing, scaling, random translation, and rotation. In the second phase of the proposed framework, three residual networks (ResNets) are used for deep feature extraction. In the third phase, these features are fused together using discrete cosine transform (DCT). In the fourth phase, three machine learning classifiers are used to perform the classification procedure. The dataset used in this study was a benchmark 2D CT dataset containing a total of 2482 CT images, with 1252 for COVID-19, while the remaining 1230 were non-COVID-19. The proposed framework was able to provide accuracy of 99.6% and an F1-score of 99.6%. The uAI Intelligent Assistant Analysis System (IAAS) is a deep-learning-based software platform developed by United Imaging Medical Technology Company Limited (Shanghai, China) for the classification of COVID-19 [65]. The IAAS software has an underlying deep learning architecture consisting of a modified 3D convolutional neural network and a combined V-Net with bottleneck structures.