COVID-19 Disease Detection using Pre-Trained Deep Convolutional Neural Network (GoogleNet) on Cloud Platform

ABSTRACT

Background and Purpose: The COVID-19 epidemics are causing the main rash in more than 151 countries around the whole world.Covid-19 has a bad effect on human life worldwide. One of the critical steps in fighting COVID-19 is finding the contaminated patients early enough and putting these infected people under special care. Our main aim is to separate COVID-19 patients from other patients. Materials and Methods: In this research article, we used GoogleNet as a learning network. GoogleNet is a deep convolutional neural network of 22 layers deep. We have used a pre-trained version of the GoogleNet trained on ImageNet. The pre-trained GoogleNet image input size is 224 x 224.GoogleNet;the deep convolutional neural network model can analyze X-ray images to classify the patient’s condition of the affected disease. Result: Experiments and evaluation of the GoogleNet have been effectively done based on 80% of X-ray pictures for training and 20% of X-ray pictures for testing phases respectively. GoogleNet shows a good result for disease classification with 91.40% of accuracy in 2.49 minutes. Conclusion: In this research paper, we have used the deep CNN model to classify COVID-19 disease using X-ray images based on the projected GoogleNet. Scientific studies will be the next goal of this research article.