Efficient COVID-19 Detection From Chest X-Ray Images Using Deep Transfer Learning

The new coronavirus, COVID-19, is causing a global pandemic that is now underway, making quick and precise diagnostic techniques necessary. The effectiveness of applying transfer learning techniques for the identification of COVID-19 in chest X-ray pictures is thoroughly examined in this research work. The research uses pre-trained convolutional neural networks (CNNs) to improve COVID-19 diagnostic performance in terms of both speed and accuracy. Using a dataset of chest X-ray pictures from patients with and without COVID-19 infection, we investigate several approaches for preprocessing, selecting models, and implementing them to have the best detection results. Our findings show that, when compared to conventional techniques, transfer learning may greatly increase diagnosis accuracy, making it a potentially useful tool for medical practitioners. The suggested model's performance in comparison to current approaches is also covered in the report, along with its therapeutic implications, any drawbacks, and suggestions for further study. Our goal in conducting this study is to add a dependable, scalable, and effective diagnostic method to the expanding body of knowledge in the fight against COVID-19.