WEENet: An Intelligent System for Diagnosing COVID-19 and Lung Cancer in IoMT Environments - Muhammad, Khan; Ullah, Hayat; Khan, Zulfiqar Ahmad; Saudagar, Abdul Khader Jilani; AlTameem, Abdullah; AlKhathami, Mohammed; Khan, Muhammad Badruddin; Abul Hasanat, Mozaherul Hoque; Mahmood Malik, Khalid; Hijji, Mohammad; Sajjad, Muhammad (Frontiers in Oncology, 2022)
The COVID-19 pandemic has caused a major outbreak around the world, having a severe impact on the health, human lives, and economy globally. One of the crucial steps in fighting COVID-19 is the ability to detect the 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 images 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.