CovRoot: COVID-19 detection based on chest radiology imaging techniques using deep learning - Niloy, Ahashan Habib; Fahim, S. M. Farah Al; Parvez, Mohammad Zavid; Shiba, Shammi Akhter; Faria, Faizun Nahar; Rahman, Md. Jamilur; Hussain, Emtiaz; Tamanna, Tasmi (Frontiers in Signal Processing, 2024)
The world first came to know the existence of COVID-19 (SARS-CoV-2) in December 2019. Initially, doctors struggled to diagnose the increasing number of patients due to less availability of testing kits. To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convolutional Neural Network (CNN). Previously some research methods were based on X-ray images and others on CT scan images. Few research methods addressed both image types, with the proposed models limited to detecting only COVID and NORMAL cases. This limitation motivated us to propose a 42-layer CNN model that works for complex scenarios (COVID, NORMAL, and PNEUMONIA VIRAL) and more complex scenarios (COVID, NORMAL, PNEUMONIA VIRAL, and PNEUMONIA BACTERIA). Furthermore, our proposed model indicates better performance than any other previously proposed models in the detection of COVID-19.