Prediction of COVID-19 using hyperparameter optimized convolutional neural network

Abstract

All over the world, there are heavy cases of COVID-19 patients those exhibiting the symptoms. In a very short period of time, this pandemic virus has become drastic across the country. A fast detection of corona spread is necessary for both the infected and uninfected person for the further spreading. The preexisting techniques used methods like Linear Regression, Support Vector Machine (SVM) and Naive Bayes are not producing better results. Our aim is to bring out better outcomes and to produce good accuracy. Instead of machine learning we opt for deep learning approaches in our work. Image preprocessing will be done by Histogram Equalization algorithm and further the image classification is done by Convolution Neural Network (CNN) architectures such as VGG-16 and ResNet-50 by using 350 images of X-ray datasets. From the comparison, VGG-16 produce better train and test accuracy of 92% and 98.4% .Hence the accuracy of VGG-16 was further tuned using Hyper Parameter Optimization using Tensor Board which produces better outcomes. © 2021 Karadeniz Technical University. All rights reserved.