A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography
Open accessAbstract
Chest X-ray radiography (CXR) is among the most frequently used medical imaging modalities. It has a preeminent value in the detection of multiple life-threatening diseases. Radiologists can visually inspect CXR images for the presence of diseases. Most thoracic diseases have very similar patterns, which makes diagnosis prone to human error and leads to misdiagnosis. Computer-aided detection (CAD) of lung diseases in CXR images is among the popular topics in medical imaging research. Machine learning (ML) and deep learning (DL) provided techniques to make this task more efficient and faster. Numerous experiments in the diagnosis of various diseases proved the potential of these techniques. In comparison to previous reviews our study describes in detail several publicly available CXR datasets for different diseases. It presents an overview of recent deep learning models using CXR images to detect chest diseases such as VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, and ensemble learning methods that combine multiple models. It summarizes the techniques used for CXR image preprocessing (enhancement, segmentation, bone suppression, and data-augmentation) to improve image quality and address data imbalance issues, as well as the use of DL models to speed-up the diagnosis process. This review also discusses the challenges present in the published literature and highlights the importance of interpretability and explainability to better understand the DL models’ detections. In addition, it outlines a direction for researchers to help develop more effective models for early and automatic detection of chest diseases.
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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 [52]
Image-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 [52]
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Cited by
Sorted by citations
Yang, Q., Bee, Y. M., Lim, C. C., Sabanayagam, C., Yim-Lui Cheung, C., Wong, T. Y., Ting, D. S. W., Lim, L.-L., Li, H., He, M., et al. (2025) · eClinicalMedicine · doi:10.1016/j.eclinm.2025.103089 · Cited by 38 · see citing works
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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 .”
Influential
- Authors
- Ait Nasser, A., & Akhloufi, M. A.
- Affiliations
- Perception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1C 3E9, Canada
- Université de Moncton (CA)
- Corresponding author
- Akhloufi, Moulay A.
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2023-01-03
- Volume
- 13
- Issue
- 1
- Pages
- 159
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC9818166
- OpenAlex
- W4313584126
- Cited by
- 121
- References
- 135
- Citations by year
- 2026: 39 · 2025: 36 · 2024: 31 · 2023: 15
- FWCI
- 5.09
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- ScriptoriUM (submittedVersion)
- Funders
- New Brunswick Health Research Foundation (NBHRF) (POF2021-006)
- New Brunswick Innovation Foundation (NBIF) (POF2021-006)
- New Brunswick Priority Occupation Student Support Fund (NBPOSS) (POF2021-006)
- 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
- chest radiography, computer-aided diagnosis, lung disease, deep learning models, VGG, ResNet, DenseNet, Inception, EfficientNet, RetinaNet, ensemble learning, image preprocessing, bone suppression, data augmentation, class imbalance, interpretability, explainability
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