## 4 Deep Learning for COVID-19 Detection and Classification
### Identification of COVID-19 in Chest CT and CXR Images Using Multimodal Deep Learning Models
The joint model achieved better performance (AUC = 0.92) compared to the CNN model trained only on CT scans (AUC = 0.86); furthermore, it outperformed the MLP model trained on clinical information (AUC = 0.80). [PERSON] et al. [81] also found that the diagnostic model composed of radiological semantic features with clinical features was significantly different. The dataset used in the experiment (CT scans and clinical information) was collected from five independent hospitals in China. A total of 136 cases were labelled as COVID-19 or non-COVID-19. The researchers identified 18 radiological semantic features and 17 clinical features, including demographic information, daily body temperature, blood pressure, heart rate, clinical symptoms, history of exposure to epidemic centers, total white blood cell (WBC) counts, lymphocyte counts, lymphocyte ratios, neutrophil count, neutrophil ratios, procalcitonin (PCT), C-reactive protein (CRP) levels, and erythrocyte sedimentation rates (ESR). To compare diagnostic performance, the authors developed three models: one for clinical features, a second for CT scan features, and a third for the proposed model that combined them. The proposed model outperformed the others, with an AUC of 0.986, while the AUC values for the clinical and CT scan feature models were 0.952 and 0.969, respectively. The combined use of CT scan images and clinical findings also occurred in another study [82]. Notably, this research also achieved promising diagnostic results for COVID-19. The study's dataset consisted of 168 patients, including 88 COVID-19-positive and 80 COVID-19-negative patients. The latter category included patients with bacterial infection, Mycobacterium tuberculosis complex, influenza virus A, influenza virus B, influenza virus B and mycoplasma, and mycoplasma. The total dataset was randomly distributed into a training subset (118 patients) and a testing subset (50 patients). The data from continuous variables within the clinical information were categorized into different classes using means, medians, and standard deviations. The categorical data were analyzed using chi-squared or Fisher's tests. Using 10 variables, logistic regression analysis was performed with ROC curves for analyzing the performance. Data from CT scans allowed the separation of visual features (i.e., the number of affected lobes and segments, segments with peripheral GGO, consolidation, air bronchograms, crazy-paving patterns, subpleural curvilinear lines, bronchiectasis, and patchy lesions), which were used to categorize between COVID-19 and non-COVID-19 patients. The predictive model utilizing clinical information with CT scan features was able to achieve good results, with an AUC of approximately 0.91. In addition, [PERSON] et al. [83] noted that there is a lack of awareness of the importance of biomedical features and choosing the right technical approach to diagnose COVID-19. Therefore, they proposed a late fusion deep learning-machine learning multimodal diagnostic approach to classify 214 patients with non-severe COVID-19, 148 patients with severe COVID-19, 129 patients with other viral infections, and 198 uninfected individuals. The data for 689 patients were collected from different hospitals in China, and the healthy cases (control group) were selected from patients who made up a regular annual physical examination cohort. For the preprocessing stage, the original image dataset was reduced to 512 \(\times\) 512 pixels. The data were randomly distributed into 80% training and 20% validation subsets. The features used in the model were clinical (23), lab testing (10), and CT scan features. A customized ResNet CNN model was used for CT scan images and applied with three different ML models--random forests, SVM, and k-nearest neighbors (kNN)--for clinical findings and lab testing. The best performance with regard to all metrics was achieved when integrating SVM with ResNet, with an overall multimodal classification accuracy of 99.8%.