Abstract
The coronavirus disease 2019 (COVID-19) pandemic has caused a major outbreak around the world with severe impact on health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect infected patients at early stages and put them under special care. Detecting COVID-19 from radiography images using computational medical imaging method is one of the fastest ways to diagnose the patients. However, early detection with significant results is a major challenge, given the limited available medical imaging data and conflicting performance metrics. Therefore, this work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images. The proposed work presents “WEENet” by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for COVID-19 diagnosis in medical image data. The performance of our method is evaluated on three benchmark medical chest x-ray and computed tomography image datasets using eight evaluation metrics including a novel strategy of cross-corpse evaluation as well as robustness evaluation, and the results are surpassing state-of-the-art methods. The outcome of this work can assist the epidemiologists and healthcare authorities in analyzing the infected medical chest x-ray and computed tomography images, management of the COVID-19 pandemic, bridging the early diagnosis, and treatment gap for Internet of Medical Things environments.
Highlights
In the beginning of December 2019, a novel infectious acute disease called coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged and caused severe impact on health, human lives, and global economy
We close this section by emphasizing on the feasibility of our proposed WEENet framework for COVID19 diagnosis in 5G-enabled Internet of Medical Things (IoMT) environments
The COVID-19 pandemic started in 2019 and has severely affected human life and the world economy for which different actions are initiated to stop its spread and efficiently handle the pandemic. Such actions include the concept of smart lockdown, development of new devices for temperature checking, early detection of COVID-19 using medical imaging techniques, and treatment plans for patients with different risk levels
Summary
In the beginning of December 2019, a novel infectious acute disease called coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has emerged and caused severe impact on health, human lives, and global economy. This COVID-19 disease originated in Wuhan city of China and spread in several other countries and become a global pandemic [1]. The most common and convenient technique for diagnosing COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) This technique has very low precision, high delay, and low sensitivity, making it less effective in preventing the spread of COVID-19 [3]
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