Improved Convolutional Neural Multi-Resolution Wavelet Network for COVID-19 Pneumonia Classification
Happy Nkanta Monday, Jianping Li, Grace Ugochi Nneji, Ariyo Oluwasanmi, Goodness Temofe Mgbejime, Chukwuebuka Joseph Ejiyi, Ijeoma Amuche Chikwendu, Edidiong Christopher James
Abstract
Fast and early detection of infected patient is the most paramount step necessary to curb the spread of the COVID-19 disease. Radiographs have perhaps presented the fastest means of diagnosing COVID-19 in patients. The well-known standard for COVID-19 test requires a standard procedure and usually has low sensitivity. Previous studies have adopted various AI-based methods in detecting COVID-19 using both chest tomography and chest x-ray. In this study, the goal is to propose an enhanced convolutional neural multi-resolution wavelet network for COVID-19 pneumonia diagnosis. Our proposed model is a convolutional neural network integrated discrete wavelet transform of four level decomposition multiresolution analysis robust to handle few dataset which is very paramount due to the fast emergence of COVID-19. We evaluated our model based on three categories of public dataset of chest x-ray and chest tomography images. Our proposed model achieves 98.5% accuracy, 99.8% sensitivity, 98.2% specificity, and 99.6% AUC for multiple class categories with less training parameters. The results of this study show that our method achieves state-of-the-art result.