Innovative Silicosis and Pneumonia Classification: Leveraging Graph Transformer Post-hoc Modeling and Ensemble Techniques
Journal:
arXiv
Published Date:
Dec 31, 2024
Abstract
This paper presents a comprehensive study on the classification and detection
of Silicosis-related lung inflammation. Our main contributions include 1) the
creation of a newly curated chest X-ray (CXR) image dataset named SVBCX that is
tailored to the nuances of lung inflammation caused by distinct agents,
providing a valuable resource for silicosis and pneumonia research community;
and 2) we propose a novel deep-learning architecture that integrates graph
transformer networks alongside a traditional deep neural network module for the
effective classification of silicosis and pneumonia. Additionally, we employ
the Balanced Cross-Entropy (BalCE) as a loss function to ensure more uniform
learning across different classes, enhancing the model's ability to discern
subtle differences in lung conditions. The proposed model architecture and loss
function selection aim to improve the accuracy and reliability of inflammation
detection, particularly in the context of Silicosis. Furthermore, our research
explores the efficacy of an ensemble approach that combines the strengths of
diverse model architectures. Experimental results on the constructed dataset
demonstrate promising outcomes, showcasing substantial enhancements compared to
baseline models. The ensemble of models achieves a macro-F1 score of 0.9749 and
AUC ROC scores exceeding 0.99 for each class, underscoring the effectiveness of
our approach in accurate and robust lung inflammation classification.
