Bias-Resilient Ensemble of Transfer Learning Models for Automated COVID-19 Detection from Chest Radiographs

The viral disease COVID-19, declared a pandemic by the World Health Organization (WHO), primarily affects the respiratory system and can be fatal. Although the Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) test remains the gold standard for COVID-19 diagnosis, its time-intensive nature limits its effectiveness in urgent situations. To address this, we propose an ensemble of five state-of-the-art transfer learning (TL) models designed to mitigate biases and enhance the classification of COVID-19 from chest radiographs. A weighted optimization strategy combines the models, giving more weight to those with superior performance, ensuring more accurate and robust predictions. Evaluated on a publicly available dataset, the SqueezeNet model achieved the highest accuracy of 94.01% for three-class classification (Normal, COVID-19, Lung Opacity), while the ensemble approach achieved 92.57% accuracy and an F1-score of 92.36%, demonstrating resilience to transfer learning biases. This framework offers reliable diagnostic support, streamlining radiology workflows and enhancing decision-making in high-demand clinical environments. Additionally, it serves as a valuable tool for advancing medical artificial intelligence expertise among graduates.