Deep Learning Model for Image-Based Diagnosis of COVID-19 Classification Using Convolution Neural Network

Abstract

                COVID-19 is a pandemic and considered as a life nasty disease. In IT community, Machine learning (ML) and Deep learning (DL) approaches can play a vital role in identifying COVID-19 patients by visual analysing their chest x-ray (CXR) images. The aim of this study was to evaluate the depth of the layer and the degree of fine-tuning of CXR-based COVID-19 transfer learning with a deep convolution neural network (CNN) to identify effective transfer learning strategies. And also to classify CXR images in two classes, COVID-19 presence or absence. Features extracted from the image of the CXR using the Gray-level difference method. And the Genetic Algorithm is used to choice the features from the extracted features. And fine tuning of the DL algorithm of the CNN classifier is used to categoriseCXR images as either positive or negative. Classification performance is improved by the use of 5-fold cross-validation techniques. To avoid over-fitting, each fold dataset was separated into self-determining training and validation sets using a split of 80 to 20 percent. The projected method was appraised using two X-ray datasets of COVID-19. The proposedtechniqueattained accuracy rates of 96.09 percent and 98.09 percent respectively for the first and second data sets.