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Gross Anatomy of the Lungs

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The lungs are a pair of vital organs connected to the trachea via the left and right bronchi. The base of these organs meets the dome-shaped muscle known as the diaphragm. Encased by the pleurae, the lungs contact the mediastinum. The right lung is shorter yet wider, and has a larger volume than the left lung. The left lung has an indentation known as the cardiac notch. The superior region of the lungs is referred to as the apex, whereas the base is the lower region near the diaphragm. The...

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Updated: Mar 28, 2026

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Graph attention network-based multimodal approach for lung diseases classification.

Muhammad Rahman¹, Cao YongZhong², Li Bin³

¹College of Information and Artificial Intelligence, Yangzhou University, Yangzhou, 225127, China.

Scientific Reports

|March 27, 2026

Summary

This study introduces a novel multimodal approach for lung disease classification, integrating chest X-rays and clinical notes. The Graph Attention Network framework achieves high accuracy, offering an efficient automated diagnostic tool.

Keywords:

Graph attention network Lung diseases classification Multimodal learning Pre-trained transformer models Transformer models for image-text classification

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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

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Published on: September 19, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Pulmonology

Background:

Lung diseases are a leading cause of global mortality.
Accurate diagnosis relies on integrating diverse data like clinical notes, symptoms, lab tests, and chest X-rays.
Manual interpretation is time-consuming and prone to errors due to data complexity.

Purpose of the Study:

To develop an automated multimodal classification framework for enhanced lung disease diagnosis.
To improve diagnostic precision and streamline clinical workflows.
To leverage Graph Attention Networks for integrating imaging and textual data.

Main Methods:

A multimodal approach using Graph Attention Network (GAN).
Integration of pre-trained Clinical ModernBERT and RAD-DINO models.
Cross-modal interaction at the token level for robust representation learning.

Main Results:

Achieved 95.73% accuracy, 95.75% precision, 95.72% recall, and 95.70% F1-score for multiclass lung disease classification.
Demonstrated a low expected calibration error of 0.0124.
Outperformed existing state-of-the-art methods in integrating imaging and textual information.

Conclusions:

The proposed GAN-based multimodal framework offers an accurate and reliable automated solution for lung disease diagnosis.
Effective integration of imaging and textual data at the token level advances diagnostic capabilities.
This approach enhances diagnostic precision and optimizes clinical workflow efficiency.