## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT Images Using Deep Learning Models
A total of 2460 images were used in this study (2215 for COVID-19, while the rest were normal). The authors failed to provide details regarding the data imbalance issues with respect to the distribution of the training and validation sets. At the same time, detailed information regarding preprocessing procedures and final performance was also not provided. One potential reason could be that the main purpose of the study was to assess the feasibility of utilizing the uAI IAAS as a diagnostic tool for COVID-19 from CT images. [PERSON] et al. [66] utilized different deep learning frameworks to differentiate between the CT images of COVID-19 and non-COVID-19 patients. CTnet-10 was designed for the diagnosis of COVID-19, developed using six successive layers of convolution and maximum pooling, followed by flattening and dropout layers, and ending in a softmax classification layer. Some of the other models that were tested included DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG 19. The performance of the different deep learning models was assessed to highlight the most suitable option for the classification of COVID-19. For the CT-net model, the input image size was 128 \(\times\) 128 pixels. For the VGG-19 model, the image dimensions used were 224 \(\times\) 224 \(\times\) 3 pixels. The images were randomly distributed into 80% for training, 10% for validation, and 10% for testing. CT-net provided an accuracy of 82.1%, while the VGG-19 model was able to provide an accuracy of 94.5%. [PERSON] et al. [67] developed a deep-learning-based framework for CT scans of COVID-19 cases. The dataset used in this study was based on 1065 CT images of COVID-19 patients as well as patients who had a prior history of typical viral pneumonia. Unfortunately, the authors did not mention the class-wise distribution of data for the different lung diseases and COVID-19, limiting the ability to assess class balance for this study. The dataset was randomly divided into one training subset (320 CT im-ages), one internal validation subset (455 CT images), and one external validation cohort (290 CT images). The proposed architecture consisted of the preprocessing module, deep feature extraction module, and final classification module. The preprocessing module involved the conversion of the original images into grayscale, followed by grayscale binarization, background area filling, color reversal, and ROI selection. The preprocessed images were rescaled to 299 \(\times\) 299 \(\times\) 3 pixels before being used for deep feature extraction. The transfer learning process involved training with a predefined model, which in this study was the GoogLeNet-Inception-v3 deep learning architecture. After feature extraction, the final step was to provide multi-class classification using an ensemble of classifiers to improve performance. The proposed model was able to provide an accuracy of 89.5% on internal validation and 79.3% on external validation, which is significantly lower than the performance provided by prior studies discussed in this section. As this review's findings indicate, many studies have been published that rely on CT scans to diagnose COVID-19. However, these studies not only suffer from limitations, but also produce models that cannot be applied practically and economically in routine clinical practice. This is especially due to the cost and limited availability of CT scans, as well as the circumstances of the ongoing pandemic. In comparison, CXR has better availability and ease of execution, and minimizes in-hospital transmission; it is neither time-consuming with lengthy waiting times, such as the CT scan procedure, nor does it require wearing a special suit. In addition, CT scans are often optimally applied in critical cases where the infected lungs are very clear for radiologists. Identification of COVID-19 from CXR Images using Deep Learning Models in a Hierarchical Classification Scenario  
One of the earliest studies that applied hierarchical image classification using deep learning was carried out in 2015 [68].