A Novel Approach for Multi-Label Chest X-Ray Classification of Common Thorax Diseases
Open accessAbstract
Chest X-ray (CXR) is one of the most common types of radiology examination for the diagnosis of thorax diseases. Computer-aided diagnosis (CAD) was developed to help radiologists to achieve diagnostic excellence in a short period of time and to enhance patient healthcare. In this paper, we seek to improve the performance of the CAD system in the task of thorax diseases diagnosis by providing a new method that combines the advantages of CNN models in image feature extraction with those of the problem transformation methods in the multi-label classification task. The experimental study is tested on two publicly available CXR datasets ChestX-ray14 (frontal view) and CheXpert (frontal and lateral views). The results show that our proposed method outperformed the current state of the art.
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The classifiers are either state of the art or near state of the art in multi-label disease classification, as measured by AUCs averaged across all labels for each dataset.
“To the best of our knowledge, our classifiers are either state of the art (SOTA) (14 labels for the CXP and CXR datasets and eight shared labels for the ALL dataset) [19][20][21][22] or near SOTA (15 labels for NIH) 22 in the multi-label disease classification task, as measured by AUCs averaged across all of the labels for each dataset.”
From Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations (2021) · cites this paper as [22]
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- Authors
- Allaouzi, I., & Ben Ahmed, M.
- Affiliations
- Abdelmalek Essaâdi University (MA)
- ORCID
- Allaouzi, Imane
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 64279-64288
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2946236639
- Cited by
- 126
- References
- 26
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 19 · 2023: 17 · 2022: 18 · 2021: 31 · 2020: 13 · 2019: 3
- FWCI
- 10.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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 › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- chest radiography, multi-label classification, computer-aided diagnosis, convolutional neural networks, image feature extraction, problem transformation, ChestX-ray14, CheXpert
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