The Capability of Multi Resolution Analysis: A Case Study of COVID-19 Diagnosis
Happy Nkanta Monday, Jianping Li, Grace Ugochi Nneji, Edidiong Christopher James, Ijeoma Amuche Chikwendu, Chukwuebuka Joseph Ejiyi, Ariyo Oluwasanmi, Goodness Temofe Mgbejime
Abstract
The outbreak of COVID-19 in more than 150 nations across the globe is severely impacting the health of people worldwide. A more reliable way to curb the spread of COVID-19 is early detection of infected patients for temporary isolation and care. Image base detection of COVID-19 presents the quickest way to diagnose patients. Few literatures have shown that chest radiograms of infected COVID-19 patients contain irregular characteristics. From related studies, we investigated the application of wavelet transform multi resolution analysis to detect COVID-19 patients using chest radiography. In this study, we proposed a wavelet based convolutional neural network to handle data scarcity in this era of COVID-19 fast emergence. We only considered four levels of wavelet transform decompositions. We utilized an open source dataset from National Institute Health containing several X-rays of pneumonia related diseases whereas the COVID-19 dataset is collected from Radiology Society North America. From the experimental results, our model achieved sensitivity greater than 90% while the specificity is above 90%. We also show the receiver operating characteristic and precision-recall curves of each decomposition level. Our results show that the performance of our proposed model is encouraging and outperformed previous state-of-the-art models.