Deep Learning Based Approaches For Low Cost Defense Detection

<p>Pulmonary fibrosis is a progressive interstitial lung disease characterized by the accumulation of fibrotic tissue within the lungs, leading to impaired respiratory function and reduced quality of life. Early detection is important for disease management; however, accurate diagnosis often relies on high-resolution computed tomography (CT), which may not be accessible in all clinical settings. Chest radiography provides a lower-cost and widely available imaging modality, but interpretation of chest X-rays for fibrotic disease can be challenging due to subtle radiographic patterns and overlapping anatomical structures. This thesis investigates the use of multimodal deep learning techniques to assist in pul- monary fibrosis detection by integrating chest X-ray images with corresponding radiology reports. Pretrained medical vision–language models, including BioViL-T, PubMedCLIP, and KAD, are used to generate embedding representations for both imaging and textual modalities. These embeddings are combined through a multimodal feature representation and processed by a neural network classifier to predict the presence of pulmonary fibro- sis. The proposed framework is evaluated using the PadChest dataset, which contains chest radiographs paired with radiology reports. Experimental results demonstrate that incorporating textual radiology reports alongside imaging features can improve diagnostic performance compared to models that rely solely on image-based representations. The findings highlight the potential of multimodal learning frameworks to better capture clinically relevant information by integrating visual imaging patterns with expert textual interpretation. This work contributes to the development of computational tools designed to support clinicians in pulmonary fibrosis assessment using widely available chest radiography data.</p>

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Math and Computer Science

Year dc:date.available

2026

Author dc:creator

• Noel-Rickert, Adele J

Contributors dc:contributor

• Vinay Shashidhar

• Pulmonary fibrosis
• Chest X-ray
• Multimodal deep learning
• Medical imaging
• Vision-language models
• Radiology reports
• Deep learning
• Medical image analysis
• Computer-aided diagnosis
• Multimodal learning
• Artificial Intelligence and Robotics
• Biomedical Informatics
• Radiology

Repository record dc:identifier

https://commons.nmu.edu/theses/926

OAI identifier oai:identifier

oai:commons.nmu.edu:theses-1972