A Performance Study on Deep Learning Covid-19 Prediction through Chest X-Ray Image with ResNet50 Model

Abstract

The COVID-19 epidemic has claimed many lives throughout the world and constitutes an unprecedented public health concern. The key challenge in early detection of the corona virus is early detection. And the main obstacle was the similarity of COVID-19 symptoms to flu symptoms. With the goal of saving human lives and stemming the spread of a worldwide pandemic, an accurate and speedy analysis of COVID-19-induced pneumonia has now taken centre stage. Responding this urgent concern and to reduce the burden as well as chances of faulty manual diagnosis, several deep learning approaches are developed to conduct early diagnosis. Based on the availability of reliable patient's records, an accepted technique is pre-trained deep learning prediction approach through patient's chest X-Rays. Convenience of this approach led development of a number of novel deep learning-based lung screening technologies. However, little emphasis is placed on ensuring the quality of their output. Pre-trained deep learning systems will be used in this project to evaluate their ability to recognise and diagnose disorders. To categorise COVID and normal pictures, a neural network-based ResNet50 architecture is presented. The implementation is based on the normal, COVID, and lung opacity datasets. For data pre-processing, ImageDataGenerator is used, which rescales, flips, and modifies the range to meet the model. To categorise the x- ray images, the suggested method ResNet50 architecture is used. Performance matrices like precision, accuracy, recall, as well as F1-score are examined to verify the algorithm's usefulness. The suggested technique has an accuracy of 80%, indicating that the proposed model is quite good in classifying COVID and normal x-ray pictures. This research will have a significant influence on real-time since it will accurately diagnose COVID in less time, perhaps lowering the mortality rate.