Analysis on Covid-19 Detection System Using Machine Learning & Deep Learning Models

Abstract

COVID-19, considered the deadliest virus of the twenty-first century, has claimed the lives of millions of people worldwide in less than two years. The respiratory disease (COVID- 19) is caused by the novel coronavirus SARS-CoV-2, which originated in Wuhan, [14] China in late December of 2019. By October 2020, the virus already infected almost 40,000,000 people dead over one million (Hopkins (2020)). This infection has rapidly expanded across China and into other nations since then, creating a global pandemic in 2020 due to its ease of transmission from person to person via respiratory droplets. Pneumonia is another infectious condition that is frequently caused by a bacterial infection in the alveoli of the lungs. When an infected lung tissue becomes inflamed, pus forms in it. Because the virus first affects the lungs of patients, X-ray imaging of the chest is useful for accurate diagnosis. To determine whether a patient has these conditions, experts conduct physical examinations and diagnose them with a chest X-ray, ultrasound, or a lung biopsy. In this analysis, we recommend using a chest X-ray to prioritize people for subsequent RT-PCR testing. It would also aid in the identification of patients with a high chance of COVID and a false-negative RT-PCR who require additional testing. It is urgent to create auto- mated technologies that could diagnose this disease in its early stages, in a non-invasive manner, and in a shorter amount of time. However, selecting the most accurate models to characterize COVID-19 patients is challenging due to the inability to compare the outputs of diverse data types and gathering methods. This is the only way to remedy the issue. As a result, much research has been conducted to establish an appropriate method for diagnosing and classifying people as COVID-19-positive, healthy, or affected by other pulmonary lung illnesses. In a few earlier scholarly works, semiautomatic machine learning techniques with limited precision were proposed. In this study, we wanted to develop reliable deep learning approaches, which are a subset of machine learning and AI that model the way humans acquire knowledge. Data science encompasses fields like statistics and predictive modeling, two of which benefit greatly from deep learning. One component of this is what are known as convolutional neural networks (CNN). Any automatic, reliable, and accurate screening strategy for COVID- 19 detection would be helpful for rapid diagnosis and reducing exposure to the virus for medical or healthcare personnel. The work takes advantage of a versatile and successful deep learning approach by employing the CNN model to predict and identify a patient as being unaffected or impacted by the disease using an image from a chest X-ray. In order to prove how well the CNN model was trained, the researchers employed a dataset consisting of 10,000 images with a resolution of 224x224 and 29 batches. Convolutional neural networks (CNNs) were demonstrated to be very effective for medical picture classification. The authors of this piece propose using convolutional neural networks (CNNs) to automatically classify chest X-ray images for signs of COVID-19. Using the dataset, eleven current CNN models— max poling operation, and SoftMax activation function—that can distinguish between COVID-19 and other lung diseases—were first used to identify the symptoms of COVID-19. A stratified 5machine learning technique was utilized with a ratio of 80 percent for training and for testing (unseen folds), and 20 percent of the training data was used as a validation set to prevent overfitting problems. During the performance training, the trained model produced an accuracy rate of 98 percent. The research study can use chest X-ray pictures to identify and de- test COVID-19, normal, and pneumonia infections, according to the results of the tests.