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

Abstract

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 Xray pictures is thoroughly examined in this research work. The research uses pretrained 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. I.INTRODUCTIONThe new coronavirus SARS-CoV-2 is the cause of the COVID-19 pandemic, which poses an unprecedented challenge to international health systems. For the virus to be contained and its effects reduced early and precise diagnosis is essential. The gold standard for COVID-19 diagnosis is reverse transcription-polymerase chain reaction (RT-PCR) testing, yet these procedures are frequently limited by time, sensitivity, and availability concerns. Consequently, there is a strong demand for complementary diagnostic techniques to RT-PCR, especially in environments with restricted resources. Because COVID-19 largely affects the respiratory system, medical imaging, particularly chest X-rays, has become an important diagnostics approach. Whenever it relates to testing, chest X-rays are quicker and easier to get than RT-PCRs. Radiologists, on the other hand, must manually examine chest X-rays, which takes time and is vulnerable to inter-observer variability. This highlights the necessity of automated, dependable, and effective diagnostic methods. Numerous uses of artificial intelligence (AI), primarily deep learning, have showed promise in the discipline of medical imaging. A effective method for COVID-19 identification is provided by transfer learning, a branch of deep learning that uses pre-trained models customised for a certain job. This strategy makes use of characteristics that have previously been learnt from big datasets. This work explores the use of transfer learning to create a reliable and effective diagnostic model for COVID-19 detection using chest X-ray images. The objective in this study is to assess how well distinct transfer learning strategies work for correctly identifying COVID-19 from chest Xray images. To choose the best course of action, we will carefully examine the dataset, preprocessing techniques,...