Abstract
The utilization of chest X-rays could offer valuable assistance in the initial
screening of patients before undergoing RT-PCR testing. This potential approach holds
promise within hospital environments grappling with the challenge of categorizing
patients for either general ward placement or isolation within designated COVID-19
zones. This study investigates the use of chest X-rays as a preliminary screening
technique for suspected COVID-19 cases in hospital settings, given the limited testing
capacity and probable delays for RT-PCR testing. We assess how well several neural
network architectures perform in automated COVID-19 identification in X-rays with
the goal of locating a model that has the highest levels of sensitivity, low latency, and
accuracy. The results reveal that InceptionV3 exhibits better robustness while
MobileNet obtains the maximum accuracy. This strategy may help healthcare
organisations better manage patients and allocate resources optimally, especially when
radiologists are hard to come by. This will help in choosing an architecture that has
better accuracy, sensitivity, and lower latency. The chosen models are pre-trained using
the technique of transfer learning to save computation power and time. After the
training and testing of the model, we observed that while MobileNet gave the best
accuracy among all the models (VGG16, VGG19, MobileNet and InceptionV3),
IncpetionV3 was still better when it comes to robustness.