Improving COVID-19 chest X-ray classification via attention-based learning and fuzzy-augmented data diversity

Abstract

This paper presents a hybrid deep learning (DL) framework that combines model-level and data-level enhancements to improve classification performance without compromising clinical relevance. The proposed framework consisted of an EfficientNetB0 model with a hybrid attention module, which focused attention both spatially and channel-wise, and a VGG-16 model that was trained on training data augmented using a fuzzy-logic-based contrast and brightness enhancement. The attention module focused the model by recalibrating the features in an adaptive manner. The fuzzy-logic augmentation increased data diversity while maintaining the anatomical fidelity of the medical image domain. In addition, an uncertainty-aware ensemble approach was utilized to combine both models' predictions, which considered model confidence and entropy of the predictions, to enhance the reliability of the predictions. The proposed framework achieves a classification accuracy of 99.6%, outperforming several existing approaches.