Abstract:
In this research paper, we build ML model to detect COVID-19 with chest x-ray image. The model was trained on a dataset of x-ray images,we trained our model using chest x-ray images. With some images showing signs of COVID-19 and others showing normal findings or other types of pneumonia. We used the Xception, ResNet50 and VGG16 model to detect the images as either COVID-19 positive or negative. Those model achieved an accuracy of 98%, 89% and 86% on the test dataset, demonstrating that, in most situations, it could correctly identify COVID-19. Due to its great accuracy, our model may prove to be a valuable tool for the quick and automatic identification of COVID-19 in chest x-ray pictures. Further studies are needed to validate the results of this research and to investigate the use of this model in real-world settings. In this study, we aimed to address the pressing need for reliable and efficient methods for detecting COVID-19, particularly in resource-limited settings where access to diagnostic tests may be limited. We believe that our machine learning model, which uses chest x-ray images as input, has the potential to play a significant role in the early detection and treatment of COVID-19. By using deep learning techniques, We were able to spot important traits in the x-ray images and utilize them to precisely categorize the images as either positive or negative for COVID-19. Our model's great accuracy raises the possibility that it could be a useful tool for healthcare professionals. and public health officials seeking to rapidly and accurately identify cases of COVID-19. Further research is needed to authenticate the enforcement of our model in different settings and to explore its potential for use in the broader fight against COVID-19.
