A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography
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,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/1/159 |
| _version_ | 1797626047217270784 |
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| author | Adnane Ait Nasser Moulay A. Akhloufi |
| author_facet | Adnane Ait Nasser Moulay A. Akhloufi |
| author_sort | Adnane Ait Nasser |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T10:05:04Z |
| format | Article |
| id | doaj.art-888bb261b5de42b9bc1641315e424164 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T10:05:04Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-888bb261b5de42b9bc1641315e4241642023-11-16T15:09:38ZengMDPI AGDiagnostics2075-44182023-01-0113115910.3390/diagnostics13010159A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using RadiographyAdnane Ait Nasser0Moulay A. Akhloufi1Perception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1C 3E9, CanadaPerception, Robotics and Intelligent Machines Research Group (PRIME), Department of Computer Science, Université de Moncton, Moncton, NB E1C 3E9, CanadaChest 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.https://www.mdpi.com/2075-4418/13/1/159radiographychest X-raycomputer-aided detectionmachine learningdeep learningdeep convolutional neural networks |
| spellingShingle | Adnane Ait Nasser Moulay A. Akhloufi A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography Diagnostics radiography chest X-ray computer-aided detection machine learning deep learning deep convolutional neural networks |
| title | A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography |
| title_full | A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography |
| title_fullStr | A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography |
| title_full_unstemmed | A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography |
| title_short | A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography |
| title_sort | review of recent advances in deep learning models for chest disease detection using radiography |
| topic | radiography chest X-ray computer-aided detection machine learning deep learning deep convolutional neural networks |
| url | https://www.mdpi.com/2075-4418/13/1/159 |
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