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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Lung Segmentation with Lightweight Convolutional Attention Residual U-Net.

Meftahul Jannat¹, Shaikh Afnan Birahim², Mohammad Asif Hasan¹

¹Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.

Diagnostics (Basel, Switzerland)

|April 12, 2025

Summary

This study introduces a new deep learning model for precise lung segmentation in chest X-rays, aiding early disease detection. The Lightweight Residual U-Net achieves high accuracy, improving diagnostic capabilities for radiologists.

Keywords:

CXR images convolutional block attention module dice loss lightweight residual U-Net lung segmentation

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Chest radiograph (CXR) analysis is complex and time-consuming, often requiring identification of multiple anomalies.
Accurate lung segmentation is crucial for improving the efficiency and performance of deep learning (DL) models in CXR analysis.
Early detection of high-risk lung diseases necessitates precise identification of lung regions in CXR images.

Purpose of the Study:

To develop a DL-based approach for accurate lung mask segmentation in CXR images.
To assist radiologists in the early identification of signs of high-risk lung diseases.
To present a novel, efficient, and accurate lung segmentation model.

Main Methods:

A novel Lightweight Residual U-Net architecture was proposed, integrating Convolutional Block Attention Module (CBAM) and Atrous Spatial Pyramid Pooling (ASPP).
The model, comprising 3.24 million parameters, was trained using LeakyReLU activation and optimized with the Dice loss function.
The technique leverages DL and attention mechanisms for enhanced lung segmentation.

Main Results:

The proposed model achieved high Dice scores on benchmark datasets: 98.72% (JSRT), 97.49% (SZ), and 99.08% (MC).
Performance surpassed existing state-of-the-art models in lung segmentation accuracy.
LeakyReLU activation and Dice loss function demonstrated superior effectiveness.

Conclusions:

The developed DL model significantly enhances lung segmentation accuracy in CXR images.
The model's efficiency and precision aid in the early detection of serious lung diseases.
This approach represents an advancement in leveraging AI for medical diagnostics.