Diagnosis and detection of COVID-19 infection on X-Ray and CT scans using deep learning based generative adversarial network

COVID-19 is presently one of the world's most serious health threats. However, PCR test kits are in poor supply, and the false-negative rate is significant in many countries. Patient triage is critical, and machine learning may be used to classify COVID-19 instances in chest X-ray or CT. X-rays scans will be utilised to extract and assess the pneumonia infection in the lungs caused by COVID-19. On the basis of GAN and FCN models, an image deep learning method is given that utilises these two models: GAN and FCN. First and foremost, the generator's network structure has been upgraded. With residual modules, convolutional learning can be more flexible in terms of how it responds to changes in the output. After reducing the sum of channels in the input feature by half, a larger convolution kernel is applied. Convolution and deconvolution layers are connected via a U-shaped network to prevent low-level info exchange. The GAN-FCN model achieved a CT scan accuracy of 94.32 percent and an X-ray picture accuracy of 95.62 percent, while existing deep learning models achieved a CT scan accuracy of almost 92 percent and an X-ray image accuracy of nearly 94 percent.Copyright © 2023 Informa UK Limited, trading as Taylor & Francis Group.

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

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.