Lung Segmentation with Lightweight Convolutional Attention Residual U-Net

Lung Segmentation with Lightweight Convolutional Attention Residual U-Net

<b>Background:</b> Examining chest radiograph images (CXR) is an intricate and time-consuming process, sometimes requiring the identification of many anomalies at the same time. Lung segmentation is key to overcoming this challenge through different deep learning (DL) techniques. Many re...

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Bibliographic Details
Main Authors: Meftahul Jannat, Shaikh Afnan Birahim, Mohammad Asif Hasan, Tonmoy Roy, Lubna Sultana, Hasan Sarker, Samia Fairuz, Hanaa A. Abdallah
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Diagnostics
Subjects:
lung segmentation
convolutional block attention module
dice loss
lightweight residual U-Net
CXR images
Online Access:https://www.mdpi.com/2075-4418/15/7/854
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Summary:<b>Background:</b> Examining chest radiograph images (CXR) is an intricate and time-consuming process, sometimes requiring the identification of many anomalies at the same time. Lung segmentation is key to overcoming this challenge through different deep learning (DL) techniques. Many researchers are working to improve the performance and efficiency of lung segmentation models. This article presents a DL-based approach to accurately identify the lung mask region in CXR images to assist radiologists in recognizing early signs of high-risk lung diseases. <b>Methods:</b> This paper proposes a novel technique, Lightweight Residual U-Net, combining the strengths of the convolutional block attention module (CBAM), the Atrous Spatial Pyramid Pooling (ASPP) block, and the attention module, which consists of only 3.24 million trainable parameters. Furthermore, the proposed model has been trained using both the RELU and LeakyReLU activation functions, with LeakyReLU yielding superior performance. The study indicates that the Dice loss function is more effective in achieving better results. <b>Results</b>: The proposed model is evaluated on three benchmark datasets: JSRT, SZ, and MC, achieving a Dice score of 98.72%, 97.49%, and 99.08%, respectively, outperforming the state-of-the-art models. <b>Conclusions:</b> Using the capabilities of DL and cutting-edge attention processes, the proposed model improves current efforts to enhance lung segmentation for the early identification of many serious lung diseases.
ISSN:2075-4418

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