Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network

Abstract

The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19’s artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.

A novel unsupervised approach based on the hidden features of Deep Denoising Autoencoders for COVID-19 disease detection

“…The main works of the second family are based on the binary classification problem of COVID/NON-COVID images ( Elmuogy et al, 2021 , Mishra et al, 2020 , Shah et al, 2021 , Tan et al, 2021 ). Specifically, the work in Shah et al (2021) is based on deep Convolutional Neural Networks (CNNs) and proposes a specific configuration called CTnet-10 while comparing the results with well-known CNN architectures, such as DenseNet-169, VGG-16, ResNet-50, InceptionV3, and VGG-19.…”

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

“…The accuracy obtained from their model is 89.5%. Tan et al (2020) hybridized super resolution generative adversarial network (SRGAN) model and VGG16 to detect infected patients by chest CT. SRGAN was used to enhance the resolution of CT images. VGG16 was used to differentiate the infected and healthy region of CT.…”

A Histogram-Based Low-Complexity Approach for the Effective Detection of COVID-19 Disease from CT and X-ray Images

“…The ML, in fact, could become a helpful and potential powerful tool for large-scale COVID-19 screening [13,65], and the author in Reference [3] has recently hypothesized that DL techniques applied to CT scans can become the first alternative screening test to the rRT-PCR in the near future. Motivated by this expectation, in the last year, the DL has been successfully used for CXRs [15,20,66,67], CT scans [56,[68][69][70], or both [18,71]. Being, indeed, challenging to summarize all the available literature in a single paper, there are some useful reviews regarding the application of DL techniques to COVID-19 detection on CXRs [72], CT scans [73,74], and both [65,75].…”

“…The approaches, which are based on segmentation, are usually founded on U-Net type architecture to identify relevant part of the CXRs/CT scans and perform classification, focusing the attention only on these sections [56,[79][80][81][82][83][84]. The second family of approaches, instead, is based on the binary classification problem of COVID/Non-COVID images [20,69,70,[85][86][87][88] and utilize deep Convolutional Neural Networks (CNNs) and their variants, including VGG16, InceptionV3, ResNet, and DenseNet.…”