Smitha Sunil Kumaran Nair, Leena R David, Abdulwahid Shariff, Saqar Al Maskari, Adhra Al Mawali, Sammy Weis, Taha Fouad, Dilber Uzun Ozsahin, Aisha Alshuweihi, Abdulmunhem Obaideen, Wiam Elshami
INTRODUCTION: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images. METHODS: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images...
April 8, 2024: Journal of Medical Imaging and Radiation Sciences