ViT-ILD: A Vision Transformer-based Neural Network for Detection of Interstitial Lung Disease from CT Images

Abstract

Interstitial Lung Disease (ILD) is a lung illness characterized by inflammation and scarring. Identifying and categorizing ILD patterns using chest Computed Tomography (CT) images is crucial for diagnosis and treatment planning. Deep learning and computer vision advancements offer the potential for automating medical image examination, such as the transformer model, which identifies intricate dependencies and relationships in data. Chest CT scans provide valuable information for ILD pattern classification and diagnosis. The Vision Transformer (ViT) based Multi-Head Self Attention (MHSA) architecture detects local and global spatial dependencies, focusing on relevant regions and considering contextual interactions. The ViT-based model architecture aims to categorize ILD patterns using MHSA mechanisms. The proposed ViT-ILD model improves the performance by modifying hyperparameters, attention heads, and hidden units. It also utilises techniques of residual connections, layer normalization, and positional encoding for improvement. The proposed method ViT-ILD has achieved comparable training, validation and test accuracy of 100%, 98%, and 82.75% respectively for the 4-class classification with a healthy lung, hypersensitivity pneumonitis, pulmonary fibrosis, and tuberculosis from the MedGift CT dataset. It is observed that the proposed ViT-ILD model has achieved test accuracy, recall, precision, and f1-score of 82.75%, 74.15%, 100%, and 82.35%.