COVID-19 Chest X-RAY detection : A transfer learning approach
AbstractThe coronavirus outbreak has caused a devastating effect on people all around the world and has infected millions. The exponential escalation of the spread of the disease makes it emergent for appropriate screening methods to detect the disease and take steps in mitigating it. The conventional testing technique involves the use of Reverse-Transcriptase Polymerase Chain Reaction (RT-PCR). Due to limited sensitivity it is more prone to providing high false negative rates. Also due to a high turnaround time (6-9 hours) and a high cost, an alternative approach for screening is called for. Chest radiographs are the most frequently used imaging procedures in radiology. They are cheaper compared to CT scans and are more readily available and accessible to the public. Application of advanced artificial intelligence (AI) techniques coupled with radiological imaging can be helpful for the accurate detection of this disease. In this paper 8 different architectures of CNN (EfficientNet, ResNet, DenseNet, VGG, Mobilenet) are compared regarding classification performance on the COVID-19 dataset. The proposed models are developed to provide accurate diagnostics for multiclass classification (Covid vs No Findings vs Pneumonia) and the best performing models are used to perform binary classification (Covid vs No Findings). Areas under the receiver operating characteristics curves (AUROC) between 0.9622 and 0.9987 could be achieved for multiclass classification while accuracy scores ranged from 90.32% to 94.93% achieving next to state-of-the-art results. A dataset was created as a mix of publicly available X-ray images from patients with confirmed COVID-19 disease, common bacterial pneumonia and healthy individuals.