EDenseNetViT: Leveraging Ensemble Vision Transform Integrated Transfer Learning for Advanced Differentiation and Severity Scoring of Tuberculosis

Abstract

Lung infections such as tuberculosis (TB), COVID-19, and pneumonia share similar symptoms, making early differentiation challenging with x-ray imaging. This can delay correct treatment and increase disease transmission. The study focuses on extracting hybrid features using multiple techniques to effectively distinguish between TB and other lung infections, proposing several methods for early detection and differentiation. To better diagnose TB, the paper presented an ensemble DenseNet with a Vision Transformer (ViT) network (EDenseNetViT). The proposed EDenseNetViT is an ensemble model of Densenet201 and a ViT network that will enhance the detection performance of TB with other lung infections such as pneumonia and COVID-19. Additionally, the EDenseNetViT is extended to predict the severity level of TB. This severity score approach is based on combined weighted low-level features and high-level features to show the severity level of TB as mild, moderate, severe, and fatal. The result evaluation was conducted using chest image datasets, that is Montgomery Dataset, Shenzhen Dataset, Chest x-ray Dataset, and COVID-19 Radiography Database. All data are merged and approx. Seven thousand images were selected for experimental design. The study tested seven baseline models for lung infection differentiation. Initially, DenseNet transfer learning models, including DenseNet121, DenseNet169, and DenseNet201, were assessed, with DenseNet201 performing the best. Subsequently, DenseNet201 was combined with Principal component analysis (PCA) and various classifiers, with the combination of PCA and random forest classifier proving the most effective. However, the EDenseNetViT model surpassed all and achieved approximately 99% accuracy in detecting TB and distinguishing it from other lung infections like pneumonia and COVID-19. The proposed EdenseNetViT model was used for classifying TB, Pneumonia, and COVID-19 and achieved an average accuracy of 99%, 98%, and 96% respectively. Compared to other existing models, EDenseNetViT outperformed the best.