Developing a Predictive Model for COVID-19 from Chest X-Ray Images Using Deep Learning Techniques

Abstract

COVID-19 is an outbreak and pandemic disease transmitted through the air and physical contact. This paper aimed to develop an automatic predicting model for COVID-19 from chest X-ray images using deep learning techniques. The techniques that were used in this study were image preprocessing and data augmentation. Two pre-trained Convolutional Neural networks (VGG16 and ResNet50) and CNN proposed model were selected to carry out 2-class prediction tasks using chest X-ray images. 80% of the chest X-ray images were used for training, while twenty percent (20%) were used to evaluate the model. The retrieved features are fitted into a neural network with 500 epochs, an 80/20 splitting ratio, and a learning rate of 0.001. The convolutional neural network model achieved with the ResNet50 of 98.16% average training accuracy compared to VGG16 with 93.65% and the proposed convolutional neural network classifier with 73.85%. The experimental result showed that the overall ResNet50 classifier yielded the highest performance evaluation of 95.4% accuracy compared to VGG16 with 93.08% and the Convolutional Neural network proposed model classifier with 55%. Future research will focus on the issue of the image number; the larger the number of images, the better the model can be trained from scratch.