Firefly Optimization with Bidirectional Gated Recurrent Unit for COVID-19 Diagnosis on Chest Radiographs

The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.