Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification
Open accessAbstract
The increased availability of labeled X-ray image archives (e.g. ChestX-ray14 dataset) has triggered a growing interest in deep learning techniques. To provide better insight into the different approaches, and their applications to chest X-ray classification, we investigate a powerful network architecture in detail: the ResNet-50. Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-ray network from scratch. To leverage the high spatial resolution of X-ray data, we also include an extended ResNet-50 architecture, and a network integrating non-image data (patient age, gender and acquisition type) in the classification process. In a concluding experiment, we also investigate multiple ResNet depths (i.e. ResNet-38 and ResNet-101). In a systematic evaluation, using 5-fold re-sampling and a multi-label loss function, we compare the performance of the different approaches for pathology classification by ROC statistics and analyze differences between the classifiers using rank correlation. Overall, we observe a considerable spread in the achieved performance and conclude that the X-ray-specific ResNet-38, integrating non-image data yields the best overall results. Furthermore, class activation maps are used to understand the classification process, and a detailed analysis of the impact of non-image features is provided.
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The use of chest radiograph images as the major imaging method is justified by their general clinical applicability and confirmed effectiveness in pneumonia and thoracic disease detection.
“The use of chest radiograph images as the major imaging method is justified by their general clinical applicability and confirmed effectiveness in pneumonia and thoracic disease detection52,58.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [58]
The Introduction (page unavailable) describes substantial clinical demand for chest X-ray readings, and the Results table reports classification performance for pneumonia and other thoracic pathologies. But the Results and Discussion and Conclusion (pages unavailable) caution that label noise makes clinical performance difficult to assess and that practical clinical use remains an open issue. The paper therefore does not establish the claimed confirmed effectiveness in clinical detection. (LLM, conf=0.84)
Experimental — accuracy not yet measuredImage-only baselines used deep convolutional neural networks for chest X-ray classification, corresponding to established literature.
“Image, only baselines made use of deep convolutional neural networks for chest X, ray classification, corresponding to radiography, based diagnostic methods already established in the literature52,58.”
From K-STAMM: a knowledge-enhanced spatial - temporal attention model with multimodal fusion for pneumonia prediction (2026) · cites this paper as [58]
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“The use of chest radiograph images as the major imaging method is justified by their general clinical applicability and confirmed effectiveness in pneumonia and thoracic disease detection 52,58 .”
- Authors
- Baltruschat, I. M., Nickisch, H., Grass, M., Knopp, T., & Saalbach, A.
- Affiliations
- University Medical Center Hamburg-Eppendorf (DE)
- Philips (Germany) (DE)
- Corresponding author
- Baltruschat, Ivo M.
- Venue
- Scientific Reports
- Published
- 2019-04-23
- Volume
- 9
- Issue
- 1
- Pages
- 6381-6381
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC6476887
- OpenAlex
- W2793854008
- Cited by
- 455
- References
- 26
- Citations by year
- 2026: 50 · 2025: 36 · 2024: 55 · 2023: 67 · 2022: 66 · 2021: 95 · 2020: 63 · 2019: 19 · 2018: 3 · 2012: 1
- FWCI
- 40.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- tub.dok (Hamburg University of Technology) (submittedVersion, cc-by)
- arXiv (Cornell University)
- TUHH Open Research (cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (FE 1045/1-1, 392323616)
- National Institutes of Health
- NIH Clinical Center
- 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
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- chest X-ray classification, multi-label classification, ResNet50, transfer learning, fine-tuning, ResNet-101, class activation maps, ChestX-ray14
- MeSH terms
- Deep Learning, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Humans, Image Processing, Computer-Assisted, Infant, Infant, Newborn, Middle Aged, Models, Theoretical, Thorax, X-Rays, Age Distribution, Statistics, Nonparametric, Area Under Curve, Databases as Topic, Young Adult
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