ABSTRACT
Chest diseases are among the most common worldwide health problems; they are potentially life-threatening disorders which can affect organs such as lungs and heart. Radiologists typically use visual inspection to diagnose chest X-ray (CXR) diseases, which is a difficult task prone to errors. The signs of chest abnormalities appear as opacities around the affected organ, making it difficult to distinguish between diseases of superimposed organs. To this end, we propose a very first method for CXR organ disease detection using deep learning. We used an ensemble learning (EL) approach to increase the efficiency of the classification of CXR diseases by organs (lung and heart) using a consolidated dataset. This dataset contains 26,316 CXR images from VinDr-CXR and CheXpert datasets. The proposed ensemble of deep convolutional neural networks (DCNN) approach achieves excellent performance with an AUC of 0.9489 for multi-class classification, outperforming many state-of-the-art models.
- Marc-André Blais and Moulay A Akhloufi. 2021. Deep Learning and Binary Relevance Classification of Multiple Diseases using Chest X-Ray images. In 43rd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC). 2794–2797.Google ScholarCross Ref
- Min Jae Cha, Myung Jin Chung, Jeong Hyun Lee, and Kyung Soo Lee. 2019. Performance of Deep Learning Model in Detecting Operable Lung Cancer With Chest Radiographs. Journal of thoracic imaging 34, 2 (2019), 86–91.Google ScholarCross Ref
- François Chollet. 2017. Xception: Deep Learning With Depthwise Separable Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1251–1258.Google ScholarCross Ref
- Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald. 2016. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association 23, 2(2016), 304–310.Google ScholarCross Ref
- Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In IEEE conference on computer vision and pattern recognition. 248–255.Google Scholar
- André Gooßen, Hrishikesh Deshpande, Tim Harder, Evan Schwab, Ivo Baltruschat, Thusitha Mabotuwana, Nathan Cross, and Axel Saalbach. 2019. Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs. arXiv preprint arXiv:1907.07324(2019).Google Scholar
- Sebastian Guendel, Sasa Grbic, Bogdan Georgescu, Siqi Liu, Andreas Maier, and Dorin Comaniciu. 2019. Learning to Recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks. In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. 757–765.Google Scholar
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770–778.Google ScholarDigital Library
- Steven Horng, Ruizhi Liao, Xin Wang, Sandeep Dalal, Polina Golland, and Seth J Berkowitz. 2021. Deep learning to quantify pulmonary edema in chest radiographs. Radiology: Artificial Intelligence 3, 2 (2021), e190228.Google Scholar
- Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4700–4708.Google Scholar
- J. Irvin, P. Rajpurkar, M. Ko, Y. Yu, S. Ciurea-Ilcus, C. Chute, H. Marklund, B. Haghgoo, R. Ball, K. Shpanskaya, J. Seekins, D. A. Mong, S. S. Halabi, J. K. Sandberg, R. Jones, D. B. Larson, C. P. Langlotz, B. N. Patel, M. P. Lungren, and A. Y. Ng. 2019. Chexpert : A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAAI conference on artificial intelligence, Vol. 33. 590–597.Google ScholarDigital Library
- Stefan Jaeger, Sema Candemir, Sameer Antani, Yì-Xiáng J Wáng, Pu-Xuan Lu, and George Thoma. 2014. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery 4, 6 (2014), 475–477.Google Scholar
- Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng. 2019. MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6, 1 (2019), 1–8.Google Scholar
- Septy Aminatul Khoiriyah, Arif Basofi, and Arna Fariza. 2020. Convolutional Neural Network for Automatic Pneumonia Detection in Chest Radiography. In 2020 International Electronics Symposium (IES). 476–480.Google Scholar
- Marciniuk, D.D. and Schraufnagel, D.E. and European Respiratory Society. 2017. The Global Impact of Respiratory Disease. European Respiratory Society.Google Scholar
- Ha Q. Nguyen, Khanh Lam, Linh T. Le, Hieu H. Pham, Dat Q. Tran, Dung B. Nguyen, Dung D. Le, Chi M. Pham, Hang T. T. Tong, Diep H. Dinh, Cuong D. Do, Luu T. Doan, Cuong N. Nguyen, Binh T. Nguyen, Que V. Nguyen, Au D. Hoang, Hien N. Phan, Anh T. Nguyen, Phuong H. Ho, Dat T. Ngo, Nghia T. Nguyen, Nhan T. Nguyen, Minh Dao, and Van Vu. 2020. VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. arXiv preprint arXiv:2012.15029(2020).Google Scholar
- Hieu H Pham, Tung T Le, Dat Q Tran, Dat T Ngo, and Ha Q Nguyen. 2021. Interpreting chest X-rays via CNNs that exploit hierarchical disease dependencies and uncertainty labels. Neurocomputing 437(2021), 186–194.Google ScholarCross Ref
- Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Yi Ding, Aarti Bagul, Curtis P. Langlotz, Katie S. Shpanskaya, Matthew P. Lungren, and Andrew Y. Ng. 2017. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning. arXiv preprint arXiv:1711.05225(2017).Google Scholar
- R. Siddiqi. 2019. Automated Pneumonia Diagnosis Using a Customized Sequential Convolutional Neural Network. In Proceedings of the 3rd International Conference on Deep Learning Technologies. 64–70.Google ScholarDigital Library
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556(2015).Google Scholar
- Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. 2015. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1–9.Google ScholarCross Ref
- Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning, Vol. 97. 6105–6114.Google Scholar
- A. Tolkachev, I. Sirazitdinov, M. Kholiavchenko, T. Mustafaev, and B. Ibragimov. 2021. Deep learning for diagnosis and segmentation of pneumothorax: the results on the kaggle competition and validation against radiologists. IEEE Journal of Biomedical and Health Informatics 25, 5(2021), 1660–1672.Google ScholarCross Ref
- Hongyu Wang and Yong Xia. 2018. Chestnet: A deep neural network for classification of thoracic diseases on chest radiography. arXiv preprint arXiv:1807.03058(2018).Google Scholar
- Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. 2017. ChestX-ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2097–2106.Google ScholarCross Ref
- Xin Wang, Evan Schwab, Jonathan Rubin, Prescott Klassen, Ruizhi Liao, Seth Berkowitz, Polina Golland, Steven Horng, and Sandeep Dalal. 2019. Pulmonary Edema Severity Estimation in Chest Radiographs Using Deep Learning. In International Conference on Medical Imaging with Deep Learning – Extended Abstract Track. https://openreview.net/forum?id=rygZBfCVqEGoogle Scholar
- Claire S Zhu, Paul F Pinsky, Barnett S Kramer, Philip C Prorok, Mark P Purdue, Christine D Berg, and John K Gohagan. 2013. The Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial and Its Associated Research Resource. JNCI: Journal of the National Cancer Institute 105, 22(2013), 1684–1693.Google ScholarCross Ref
Recommendations
Classification of CXR Chest Diseases by Ensembling Deep Learning Models
2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI)Chest related diseases are the most frequent health issues worldwide. They are mainly diagnosed by radiologists using a visual inspection of a chest X-ray (CXR) radiography. This task is challenging and error-prone because of the similarity between signs ...
A Systematic Review: Classification of Lung Diseases from Chest X-Ray Images Using Deep Learning Algorithms
AbstractThe purpose of this survey is to provide a comprehensive review of the most recent publications on lung disease classification from chest X-ray images using deep learning algorithms. Methods: This research aims to present several common chest ...
Deep Learning Methods for Chest Disease Detection Using Radiography Images
AbstractX-ray images are the most widely used medical imaging modality. They are affordable, non-dangerous, accessible, and can be used to identify different diseases. Multiple computer-aided detection (CAD) systems using deep learning (DL) algorithms ...
Comments