Developing a Dual-Stage Vision Transformer Model for Lung Disease Classification
This work addresses the need for accurate and timely diagnosis of lung diseases, which affect over 34 million people in the United States, by applying an AI method to medical imaging.
The researchers tackled the problem of classifying 14 different lung diseases from X-ray scans by developing a dual-stage vision transformer model, achieving an accuracy of 92.06% on an unseen testing subset.
Lung diseases have become a prevalent problem throughout the United States, affecting over 34 million people. Accurate and timely diagnosis of the different types of lung diseases is critical, and Artificial Intelligence (AI) methods could speed up these processes. A dual-stage vision transformer is built throughout this research by integrating a Vision Transformer (ViT) and a Swin Transformer to classify 14 different lung diseases from X-ray scans of patients with these diseases. The proposed model achieved an accuracy of 92.06% on a label-level when making predictions on an unseen testing subset of the dataset after data preprocessing and training the neural network. The model showed promise for accurately classifying lung diseases and diagnosing patients who suffer from these harmful diseases.