LungNet-ViT: Efficient lung disease classification using a multistage vision transformer model from chest radiographs.

Abstract

This research introduces a Multistage-Vision Transformer (Multistage-ViT) model for precisely classifying various lung diseases using chest radiographic (CXR) images. The dataset in the proposed method includes four classes: Normal, COVID-19, Viral Pneumonia and Lung Opacity. This model demonstrates its efficacy on imbalanced and balanced datasets by enhancing classifier accuracy through deep feature extraction. It integrates backbone models with the ViT architecture, creating rigorously hybrid configurations compared to their standalone counterparts. These hybrid models utilize optimized features for classification, significantly improving their performance. Notably, the multistage-ViT model achieved accuracies of 99.93% on an imbalanced dataset and 99.97% on a balanced dataset using the InceptionV3 combined with the ViT model. These findings highlight the superior accuracy and robustness of multistage-ViT models, underscoring their potential to enhance lung disease classification through advanced feature extraction and model integration techniques. The proposed model effectively demonstrates the benefits of employing ViT for deep feature extraction from CXR images.