SDFN: Segmentation-based deep fusion network for thoracic disease classification in chest X-ray images
Open accessAbstract
This study aims to automatically diagnose thoracic diseases depicted on the chest x-ray (CXR) images using deep convolutional neural networks. The existing methods generally used the entire CXR images for training purposes, but this strategy may suffer from two drawbacks. First, potential misalignment or the existence of irrelevant objects in the entire CXR images may cause unnecessary noise and thus limit the network performance. Second, the relatively low image resolution caused by the resizing operation, which is a common pre-processing procedure for training neural networks, may lead to the loss of image details, making it difficult to detect pathologies with small lesion regions. To address these issues, we present a novel method termed as segmentation-based deep fusion network (SDFN), which leverages the domain knowledge and the higher-resolution information of local lung regions. Specifically, the local lung regions were identified and cropped by the Lung Region Generator (LRG). Two CNN-based classification models were then used as feature extractors to obtain the discriminative features of the entire CXR images and the cropped lung region images. Lastly, the obtained features were fused by the feature fusion module for disease classification. Evaluated by the NIH benchmark split on the Chest X-ray 14 Dataset, our experimental result demonstrated that the developed method achieved more accurate disease classification compared with the available approaches via the receiver operating characteristic (ROC) analyses. It was also found that the SDFN could localize the lesion regions more precisely as compared to the traditional method.
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Segmentation-based deep fusion network achieved better classification performance compared to other approaches based on ROC for thoracic disease classification on CXR images.
“The method achieved better classification performance compared to the other approaches based on ROC [169].”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [169]
Lesion detection using CAM was found to be reliable.
“The lesion detection was assessed using CAM and was found to be reliable [169].”
From Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks (2023) · cites this paper as [169]
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- Authors
- Liu, H., Wang, L., Nan, Y., Jin, F., Wang, Q., & Pu, J.
- Affiliations
- University of Pittsburgh (US)
- Tang Du Hospital (CN)
- Fourth Hospital of Hebei Medical University (CN)
- Corresponding author
- Pu, Jiantao
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 75
- Pages
- 66-73
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- PMC
- PMC6620129
- OpenAlex
- W2949698495
- Cited by
- 135
- References
- 27
- Citations by year
- 2026: 10 · 2025: 12 · 2024: 19 · 2023: 21 · 2022: 29 · 2021: 33 · 2020: 7 · 2019: 4
- FWCI
- 12.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Institutes of Health (NIH) (R21CA197493, R01HL096613)
- Jiangsu Natural Science Foundation (BK20170391)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- thoracic disease classification, chest X-ray images, lung segmentation, feature fusion, lesion localization, small lesion detection
- MeSH terms
- Deep Learning, Algorithms, Humans, ROC Curve, Thoracic Diseases, Radiography, Thoracic, Neural Networks, Computer, Datasets as Topic
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