Demonstrates improvements in CXR classification using adaptive attention and hybrid CNN-LSTM models, implying better diagnostic strategies for COVID-19.
Key Points
The study investigates how the Self-Adaptive Convolutional Block Attention Module (SA-CBAM) enhances CXR classification performance.
Used a U-Net model for lung region segmentation from the COVID-QU-Ex dataset.
Compared various models, including CNN, attention mechanisms, and SA-CBAM.
Evaluated hybrid architectures combining CNN with LSTM networks.
Assessed models using metrics like accuracy, recall, specificity, F1-score, and MCC on segmented and unsegmented CXR images.