The COVID-19 pandemic has caused a global health crisis, leading to an urgent need for rapid and accurate virus detection. Deep learning models have been proposed as a promising solution for COVID-19 detection. In this study, we propose the use of Inception V3, a deep convolutional neural network, for COVID-19 detection based on chest X-ray images. The model was trained on a dataset of chest X-ray images from COVID-19-positive and negative patients. Our results demonstrate that Inception V3 achieved an accuracy of 95% in detecting COVID-19 from chest X-ray images. The proposed model could be used as an effective tool for the early detection of COVID-19 in high-risk populations. However, the model's reliance on chest X-ray images and potential limitations in distinguishing COVID-19 from other respiratory illnesses should be taken into consideration. In conclusion, our study suggests that deep learning models, such as Inception V3, hold promise for improving COVID-19 diagnosis and management.

Keywords—Covid-19, Deep Learning, Inception V3, Recognition System