## 2 Related Work  
Although COVID-19 has only just begun to spread, researchers have created a substantial number of research approaches in such a short time. To detect and classify COVID-19 images, which is still under research and needs further improvements, many ML, hybrid, and DL approaches have been presented in the literature. To highlight the critical literature work in COVID-19 detection and classification, below we discuss and analyze several related works on COVID-19. [PERSON] et al. [36] used multi-level thresholding and an SVM approach to identify and detect COVID-19. They used a median filter to increase the input image contrast after analyzing the patient's lung radiograph image. The Otsu objective function is then utilized to create a multi-level picture segmentation threshold. Next, the SVM was employed to distinguish between diseased and uninfected lungs. Using the proposed model, the authors achieved high average accuracy for classifying lung radiograph images than the existing approaches. [PERSON] et al. [37] introduced a research method based on the least-squares SVM (LS-SVM) and autoregressive integrated moving average (ARIMA) to detect and identify COVID-19. The study's research data were obtained from the US, UK, Italy, France, and Spain, where most confirmed coronavirus cases occurred. The authors used different feature extraction methods to increase the performance of the proposed approach. The dataset is then fed into the algorithm that predicts the disease's spread one month ahead of time. LS-SVM outperformed ARIMA in terms of accuracy. Based on the locality-weighted learning and self-organization map (LWL-SOM), [PERSON] et al. [38] proposed a novel COVID-19 detection method. They employed the SOM approach to group pictures from the chest radiographs dataset based on similar features in distinct clusters to distinguish between COVID-19 and normal patients. The LWL approach is then used to create a model for detecting COVID-19. The proposed model improved the correlation coefficient performance outcomes between COVID-19, normal, and pneumonia cases; pneumonia and normal cases; COVID-19 and pneumonia cases; and COVID-19 and normal cases. The proposed model improved the correlation coefficient performance outcomes. Current ML-based methods that use AI evaluation measures to distinguish COVID-19 and normal patients outperformed the proposed model. In [39], the authorspresent a fusion scheme based on an ML system using three significant texture features, namely, local binary pattern (LBP), fractal dimension (FD), and grey level co-occurrence matrices (GLCM). In experimental results, to demonstrate the efficiency of the proposed scheme, we have collected 300 CT scan images from a publicly available database. Furthermore, traditional ML approaches perform poorly compared to DL techniques because they rely heavily on manual feature extraction and adequate feature selection. In contrast, DL approaches use unstructured data, extract more robust deep features, and produce more accurate results than traditional ML algorithms. Recently, DL algorithms have been frequently employed to extract classification features automatically. Classifiers based on DL can be utilized to develop fully automated classifiers that can detect COVID-19 using chest radiograph images. Recently, [PERSON] et al. [40] proposed a binary classifier that recognizes COVID-19 and normal chest radiograph pictures and a multi-class classifier that detects COVID-19, pneumonia, and normal images using the DarkNet transfer learning (TL) architecture. Similarly, the Xception TL approach was utilized as a pre-trained network by [PERSON] et al. [41]. The tests were carried out on publicly available datasets. [PERSON] et al. [42] used the MobileNet DL algorithm, trained the model from scratch, and extracted various classification features for COVID-19 classification. On the COVID-19 diagnostic, [PERSON] et al. [43] used the Bayesian optimization method to optimize the SqueezeNet network.