## 2 Related Work
The COVID-19 images dataset has also been improved to improve its performance. Additionally, [PERSON] et al. [44] employed eleven CNN models to classify chest radiograph images as belonging to healthy persons, people with COVID-19, or people with viral pneumonia. They analyzed three distinct improvements to modify the frameworks for the COVID-19 detection by expanding them with extra layers. All of the investigated networks are established frameworks that have been demonstrated to be effective in image detection and classification tasks. The proposed techniques were tested on a COVID-19 radiography database for all of the studied designs, with the EfficientNetB4 and Xception-based models providing the best classification results. [PERSON] et al. [45] suggested a CNN-based model for detecting COVID-19 from chest radiograph images, making the test more effective and trustworthy. The proposed model used a TL technique and a bespoke model to improve accuracy. The pre-trained CNN models, such as InceptionV3, MobileNetV2, ResNet50, and VGG16, were utilized to extract deep features. The categorization and classification accuracy were utilized as a criterion for gauging performance in this study. According to the results of this study, DL can detect SARS-CoV-2 from CXR images. Out of all of these TL models, InceptionV3 has achieved the highest accuracy. Aside from ML approaches and DL models, previous research has also used hybrid models that combine both classic ML and DL-based methodologies. [PERSON] et al. [46] employed chest radiograph images to detect COVID-19 infected patients (deep feature and SVM-based approach) using a hybrid approach. For classification, SVM is used rather than a DL-based classifier because the latter requires a large training dataset. Deep features from the DL models' fully connected (FC) layers are obtained and fed into the SVM for COVID-19 categorization and classification. The distant chest radiograph data sources used in the technique are pneumonia, normal, and COVID-19. The method aids medical practitioners in distinguishing between healthy people, COVID-19, and pneumonia patients. The SVM algorithm is evaluated for COVID-19 detection using the attributes of 13 DL frameworks. The best results were from ResNet50 and SVM algorithms. Based on chest radiograph image data, [PERSON] et al. [47] employed CNN architectures as feature extractors and the SVM as a classification tool to determine if the participants were normal, COVID-19 positive, or had pneumonia. The tests contrasted the kernel used, feature selection strategies, feature extraction frameworks, and different classes. The authors used resnet50, resnet18, resnet101, and googlenet TL approaches to separate three classes: normal, pneumonia, and COVID-19. They attained the maximum average accuracy using resnet50, resnet18, resnet101, and googlenet. The studies mentioned above could be increased even further. Both the images sent to the model and the network's architecture are beneficial in diagnosing COVID-19 infection. As seen above, traditional ML, CNN, transfer learning, and hybrid algorithms have all been employed to detect COVID-19. The primary goal of this study is to obtain satisfactory results in detecting COVID-19 instances while avoiding false positives. When the findings are analyzed, it is clear that the proposed method is effective and simple for COVID-19 detection.