Early identification of Cystic Fibrosis based on the Vision Transformer Network

Abstract

Cystic fibrosis is a genetic disorder that primarily affects the respiratory and digestive systems, necessitating early and accurate diagnosis for effective management. This study presents a novel approach for the detection of Cystic Fibrosis (CF) leveraging Vision Transformer (ViT) technology. The study explores the integration of ViT-based frameworks, showcasing their efficacy in medical image analysis. The proposed model, named CFVision, employs ViTs to capture intricate patterns indicative of CF in radiographic images.The methodology involves pre-processing chest radiographs and fine-tuning a ViT architecture to recognize specific features associated with CF-affected lung tissues. The model's performance is evaluated on a comprehensive dataset, demonstrating its ability to accurately detect early stages of CF and distinguish them from non-CF conditions. Results indicate that the ViT-based CFVision model achieves competitive performance metrics, including high sensitivity and specificity. The abstract discusses the potential clinical implications of such an advanced diagnostic tool, emphasizing its role in early CF detection, enabling timely intervention and improved patient outcomes.This research contributes to the growing body of literature on leveraging state-of-the-art deep learning architectures, such as Vision Transformers, for the early detection of pulmonary diseases like Cystic Fibrosis.