Abstract:
Getting chest X-ray images classified quickly and accurately is vital for spotting
COVID-19, viral pneumonia, and lung opacity, especially in places where medical
resources are tight. In this study, we put together a solid system using the opensource EfficientNet-B7 model, tweaked on a public dataset with 21,165 X-ray
images covering COVID-19, lung opacity, normal cases, and viral pneumonia. We
used 5-fold cross-validation, data augmentation via Albumentations, and test-time
augmentation with rotations, hitting an average test accuracy of 96.21% and an
ensemble accuracy of 98.22%, plus a macro F1-score of 98.48% for all classes. GradCAM heatmaps help explain what the model focuses on, like opacities, making it
more useful for doctors, while Monte Carlo Dropout gives uncertainty estimates
(standard deviation between 0.0055 and 0.0076) for trustworthy results. We also
pruned and quantized the model to make it work on edge devices. Built in PyTorch,
this open-source solution offers a scalable, interpretable tool for multi-class chest
radiography, with future validation planned on external datasets
