Enhancing Low Quality in Radiograph Datasets Using Wavelet Transform Convolutional Neural Network and Generative Adversarial Network for COVID-19 Identification
Grace Ugochi Nneji, Jingye Cai, Deng Jianhua, Happy Nkanta Monday, Ijeoma Amuche Chikwendu, Ariyo Oluwasanmi, Edidiong Christopher James, Goodness Temofe Mgbejime
Abstract
The coronavirus disease of 2019 (COVID-19) pandemic has caused a global public health epidemic since there is no 100% vaccine to cure or prevent the further spread of the virus. With the ever-increasing number of new infections, creating automated methods for COVID-19 identification of Chest X-ray images is critical to aiding clinical diagnosis and reducing the time-consumption for image interpretation. This paper proposes a novel joint framework for accurate COVID-19 identification by integrating an enhanced super-resolution generative adversarial network with a noise reduction filter bank of wavelet transform convolutional neural network on both Chest X-ray and Chest Tomography images for COVID-19 identification. The super-resolution utilized in this study is to enhance the image quality while the wavelet transform Convolutional Neural Network architecture is used to accurately identify COVID-19. Our proposed architecture is very robust to noise and vanishing gradient problem. We used public domain datasets of Chest x-ray images and Chest Tomography to train and check the performance of our COVID-19 identification task. This experiment shows that our system is consistently efficient by accuracy of 0.988, sensitivity of 0.994, and specificity of 0.987, AUC of 0.99, F1-score of 0.982 and 0.989 for precision using the Chest X-ray dataset while for Chest Tomography dataset, an accuracy of 0.978, sensitivity of 0.981, and specificity of 0.979, AUC of 0.985, F1-score of 0.961 and precision of 0.980. These performances have also outweighed other established state-of-the-art learning methods.