COVID19 to Pneumonia: Multi Region Lung Severity Classification Using CNN Transformer Position-Aware Feature Encoding Network

초록

This study investigates utilizing chest X-ray (CXR) data from COVID-19 patients for classifying pneumonia severity, aiming to enhance prediction accuracy in COVID-19 datasets and achieve robust classification across diverse pneumonia cases. A novel CNN-Transformer hybrid network has been developed, leveraging position-aware features and Region Shared MLPs for integrating lung region information. This improves adaptability to different spatial resolutions and scores, addressing the subjectivity of severity assessment due to unclear clinical measurements. The model shows significant improvement in pneumonia severity classification for both COVID-19 and heterogeneous pneumonia datasets. Its adaptable structure allows seamless integration with various backbone models, leading to continuous performance improvement and potential clinical applications, particularly in intensive care units.

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