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Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis
Shilu Kang1, Dongfang Li1, Jiaxin Xu1
1Information Engineering College, Henan University of Science and Technology, Luoyang 471000, China.
Sensors (Basel, Switzerland)
|August 14, 2025
Summary
A new segmentation-assisted method improves chest X-ray classification by accurately segmenting lung fields using Partial Convolutional Segmentation Network (PCSNet) and fusing results with original images for enhanced diagnosis of lung diseases.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Accurate classification of chest X-rays (CXRs) is vital for diagnosing lung diseases.
- Existing deep learning models struggle to differentiate non-lung features in CXR images.
Purpose of the Study:
- To propose a novel segmentation-assisted fusion-based classification method for CXR images.
- To enhance the accuracy and efficiency of lung disease diagnosis using deep learning.
Main Methods:
- A lightweight segmentation model, Partial Convolutional Segmentation Network (PCSNet), was developed using an encoder-decoder architecture.
- PCSNet generates lung masks from CXR images, which are then fused with original images.
- Classification is performed using an improved lightweight ShuffleNetV2 model on the fused images.
Main Results:
- PCSNet demonstrated high segmentation performance on CXR datasets (MC, SH), outperforming seven other models.
- PCSNet achieved superior accuracy (98.94%) and boundary accuracy (97.86%) compared to Attention-Net with 62% fewer parameters.
- The proposed method improved pneumonia classification accuracy by 0.14% (98.55%) on the CXIP dataset and COVID-19 classification accuracy by 0.1% (97.50%) on the COVIDx dataset, with significant specificity gains.
Conclusions:
- The segmentation-assisted fusion method effectively improves CXR classification accuracy.
- PCSNet offers a computationally efficient and highly accurate solution for lung disease diagnosis from medical images.
- The proposed approach demonstrates clinically meaningful improvements over state-of-the-art methods in medical image analysis.
