Assessment of Chest X-Ray Image-Based COVID-19 Detection using Deep Transfer Learning Models
Paper ID: 7840
Wuhan is the city in China where COVID-19 was first discovered, and the disease quickly spread throughout the world, affecting over 215 million people. Vaccination has been tried to control the disease’s effects. Many data scientists contributed and analyzed the disease using chest X-Rays and Computed Tomography (CT) scans to control it. The data collected from Chest X-rays have been proven to be extremely effective for screening COVID-19 patients, particularly in terms of resolving overcapacity in emergency departments and urgent-care centers. Our proposed approach towards Covid-19 research contribution consists of four transfer learning models i.e., MobileNet, DenseNet201, InceptionNetV2 and NasNetMobile. Grayscale images of chest X-Rays that have been preprocessed are fed into these models as input data. The dataset used in the proposed framework is the COVID19 Radiography Database, which is available to all researchers on the Kaggle platform and contains four different types of chest X-ray images i.e., Covid-19, Pneumonia, Opacity, and Normal. For multiclass classification that is MobileNet, DenseNet201, InceptionNetV3 and NasNetMobile the models showed an impressive accuracy of 91.26%, 90.38%, 89.27, and 87.74, while for binary class classification, the prediction capability of our used models is 97.03%, 96.78%, 95.18% and 95.40% respectively.