A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity

Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severity requires a radiologist with significant expertise. This study introduces a pioneering utilization of deep learning (DL) for evaluate COVID-19 severity using lung CT images, presenting a novel and effective method for assessing the severity of pulmonary manifestations in COVID-19 patients. Inception-Residual networks (Inception-ResNet), advanced hybrid models known for their compactness and effectiveness, were used to extract relevant features from CT scans. Inception-ResNet incorporates the dilated mechanism into its ResNet component, enhancing its ability to accurately classify lung involvement stages. This study demonstrates that dilated residual networks (dResNet) outperform their non-dilated counterparts in image classification tasks, as their architectural designs allow the systems to acquire comprehensive global data by expanding their receptive fields. Our study utilized an initial dataset of 1548 human thoracic CT scans, meticulously annotated by two experienced specialists. Lung involvement was determined by calculating a percentage based on observations made at each scan. The hybrid methodology successfully distinguished the ten distinct severity levels associated with COVID-19, achieving a maximum accuracy of 96.40%. This system demonstrates its effectiveness as a diagnostic framework for assessing lung involvement in COVID-19-affected individuals, facilitating disease progression tracking.