## 2 Related Work  
In this section we discuss the related works for COVID-19 detection using various learning techniques with their limitations which pave the way for our proposed scheme. [PERSON] [20] generated a CNN based on transfer learning consisting of pretrained weights. The CNN model was used to classify between COVID-19, pneumonia and healthy patients using the chest X-ray images. The CNN architecture was based on Xception followed by ImageNet for pertained weights. The model reached an accuracy of 0.91 by detecting SARS-CoV-2 and pneumonia correctly. The model resulted in greater accuracy, but images used for the testing of the model were from already trained data. [PERSON] [21] developed a framework based on deep learning known as DL-CRC to differentiate between COVID-19, pneumonia and normal chest radiographs. The framework also performs data augmentation using GAN and generic augmentation techniques to generate synthetic COVID-19 infected chest X-ray images. Data augmentation helped to tackle the class imbalance issue and improved the accuracy of the model to 93.94%. However, the framework could not be used to classify the different stages of COVID-19, such as asymptomatic, mild, high, and severe due to shortage of data [22]. [PERSON] [23] proposed a CNN model based on CheXNet algorithm to distinguish between COVID-19 and normal chest X-rays. Two experiments were considered to train the model which resulted in an accuracy of 84% and 89.7%. It was claimed that the model has the potential role to quickly identify patients, which could be helpful and effective in mitigating the COVID-19 outbreak. However, no preprocessing was considered which could have increased the accuracy of the developed model. Mentioning ROC and confusion matrix could have been considered to further clarify the accuracy of model. [PERSON] [24] developed a CNN deep learning model to detect COVID-19 using patients X-ray images. The model further contains a patient and a cloud layer to track the patient using sensors and a mobile app. The CNN model reached an accuracy of 97.95%, making it efficient enough to contribute in improving patient quality care. It was claimed that the main purpose of the study was to remove the gap between healthcare systems and technologies. However, there is a lack of collected data for real-time COVID-19 tracking causing system to place a greater focus on image processing. A study conducted by [PERSON] [25] trialed ten popular CNN models on two publicly available datasets. Their findings showed that VGG19 displayed promising accuracy of 98.87% and the accuracy of patient status classification only dipped to 95.91%, when it miscategorized some records, compared to other techniques which resulted with better accuracy. The experiments reveal that using the same model on different datasets without proper fine-tuning leads to lower accuracy. As the study suggests that preprocessing combined with data standardization and normalization can enhance model accuracy. [PERSON] [26] developed a CNN model by using multiple imaging datasets (X-rays, CT scans and ultrasound). The experiment was conducted VGG19 model on the datasets and due to limited dataset size, they performed data preprocessing to fine-tunetheir data, which highly improved the accuracy of COVID-19 detection. The study reveals that by using VGG19 and fine-tuned data, the ultrasound dataset yielded 100% accuracy compared to 86% for X-rays and 84% for CT scans. The study showed how the lack of publicly available datasets and nonstandardization of data can affect results and that model training requires a lot of preprocessing and fine-tuning. [PERSON] [27] developed end-to-end deep learning on chest X-rays to predict the severity of lung compromise in patients. The study used Brixia Score to monitor patients and used a BSNet architecture model, which performed segmentation in lungs to determine severity.