A systematic review: Deep learning-based methods for pneumonia region detection

Research Article
Open access

A systematic review: Deep learning-based methods for pneumonia region detection

Xinmei Xu 1*
  • 1 Shenzhen College of International Education    
  • *corresponding author suzy.xu2024@outlook.com
Published on 23 October 2023 | https://doi.org/10.54254/2755-2721/22/20231219
ACE Vol.22
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-035-6
ISBN (Online): 978-1-83558-036-3

Abstract

Pneumonia disease is one of the leading causes of death among children and adults worldwide. In the last ten years, computer-aided pneumonia detection methods have been developed to improve the efficiency and accuracy of the diagnosis process. Among those methods, the effects of deep learning approaches surpassed that of other traditional machine learning methods. This review paper searched and examined existing mainstream deep-learning approaches in the detection of pneumonia regions. This paper focuses on key aspects of the collected research, including their datasets, data processing techniques, general workflow, outcomes, advantages, and limitations. This paper also discusses current challenges in the field and proposes future work that can be done to enhance research procedures and the overall performance of deep learning models in detecting, classifying, and localizing infected regions. This review aims to offer an insightful summary and analysis of current research, facilitating the development of deep learning approaches in addressing treatable diseases.

Keywords:

Pneumonia detection, CNN, Transfer learning

Xu,X. (2023). A systematic review: Deep learning-based methods for pneumonia region detection. Applied and Computational Engineering,22,210-217.
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References

[1]. World Health Organization: WHO. Pneumonia [EB/OL]. Who.int. World Health Organization: WHO2019-08-02. https://www.who.int/en/news-room/fact-sheets/detail/pneumonia.

[2]. Kelly B. The chest radiograph[J]. The Ulster medical journal, 2012, 81(3): 143.

[3]. Sharma A, Raju D, Ranjan S. Detection of pneumonia clouds in chest X-ray using image processing approach[C]//2017 Nirma University International Conference on Engineering (NUiCONE). IEEE, 2017: 1-4

[4]. Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning[J]. arXiv preprint arXiv:1711.05225, 2017.

[5]. Hayden G E, Wrenn K W. Chest Radiograph vs. Computed Tomography Scan in the Evaluation for Pneumonia[J]. The Journal of Emergency Medicine, 2009, 36(3): 266–270.

[6]. Wang X, Peng Y, Lu L, et al. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases[J]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[7]. National Institutes of Health. NIH Chest X-rays [EB/OL]. www.kaggle.com. 2017. https://www.kaggle.com/datasets/nih-chest-xrays/data.

[8]. Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning[J]. arXiv preprint arXiv:1711.05225, 2017.

[9]. RSNA Pneumonia Detection Challenge [EB/OL]. kaggle.com. /2023-01-23. https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge.

[10]. Gabruseva T, Poplavskiy D, Kalinin A. Deep learning for automatic pneumonia detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020: 350-351

[11]. Mooney P. Chest X-Ray Images (Pneumonia)[EB/OL]. www.kaggle.com. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.

[12]. Amyar A, Modzelewski R, Li H, et al. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation[J]. Computers in Biology and Medicine, 2020, 126: 104037.

[13]. Yang X, He X, Zhao J, et al. COVID-CT-dataset: a CT scan dataset about COVID-19[J]. arXiv preprint arXiv:2003.13865, 2020.

[14]. COVID-19 Lung CT Scans[EB/OL]. www.kaggle.com. https://www.kaggle.com/datasets/luisblanche/covidct.

[15]. COVID-19[EB/OL]. Medical segmentation. http://medicalsegmentation.com/covid19/.

[16]. Rajpurkar P, Irvin J, Ball R L, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists[J]. PLoS medicine, 2018, 15(11): e1002686.

[17]. Kastrama E, Pinkerton R, Samuel-Gama K. Localization of Radiographic Evidence for Pneumonia[R].

[18]. Jaiswal A K, Tiwari P, Kumar S, et al. Identifying pneumonia in chest X-rays: A deep learning approach[J]. Measurement, 2019, 145: 511-518.

[19]. Sirazitdinov I, Kholiavchenko M, Mustafaev T, et al. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database[J]. Computers & electrical engineering, 2019, 78: 388-399.

[20]. Bhandary A, Prabhu G A, Rajinikanth V, et al. Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images[J]. Pattern Recognition Letters, 2020, 129: 271-278.

[21]. Armato S G. rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans[J]. Med Phys, 2011, 38(2): 915-31.

[22]. Varshni D, Thakral K, Agarwal L, et al. Pneumonia detection using CNN based feature extraction[C]//2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). IEEE, 2019: 1-7.

[23]. Kermany D S, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. cell, 2018, 172(5): 1122-1131. e9.

[24]. Kermany D S, Goldbaum M, Cai W, et al. Labeled optical coherence tomography (OCT) and chest X-ray images for classification[EB/OL]. Mendeley Data, Mendeley Data, 2018-01-06. (2018-01-06)[2023-06-20]. https://data.mendeley.com/datasets/rscbjbr9sj/2.

[25]. Meng Z, Meng L, Tomiyama H. Pneumonia diagnosis on chest X-rays with machine learning[J]. Procedia Computer Science, 2021, 187: 42-51.

Cite this article

Xu,X. (2023). A systematic review: Deep learning-based methods for pneumonia region detection. Applied and Computational Engineering,22,210-217.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 5th International Conference on Computing and Data Science

ISBN：978-1-83558-035-6(Print) / 978-1-83558-036-3(Online)
Editor：Alan Wang, Marwan Omar, Roman Bauer
Conference website: https://2023.confcds.org/
Conference date: 14 July 2023
Series: Applied and Computational Engineering
Volume number: Vol.22
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. World Health Organization: WHO. Pneumonia [EB/OL]. Who.int. World Health Organization: WHO2019-08-02. https://www.who.int/en/news-room/fact-sheets/detail/pneumonia.

[2]. Kelly B. The chest radiograph[J]. The Ulster medical journal, 2012, 81(3): 143.

[3]. Sharma A, Raju D, Ranjan S. Detection of pneumonia clouds in chest X-ray using image processing approach[C]//2017 Nirma University International Conference on Engineering (NUiCONE). IEEE, 2017: 1-4

[4]. Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning[J]. arXiv preprint arXiv:1711.05225, 2017.

[5]. Hayden G E, Wrenn K W. Chest Radiograph vs. Computed Tomography Scan in the Evaluation for Pneumonia[J]. The Journal of Emergency Medicine, 2009, 36(3): 266–270.

[6]. Wang X, Peng Y, Lu L, et al. ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases[J]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

[7]. National Institutes of Health. NIH Chest X-rays [EB/OL]. www.kaggle.com. 2017. https://www.kaggle.com/datasets/nih-chest-xrays/data.

[8]. Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest X-rays with deep learning[J]. arXiv preprint arXiv:1711.05225, 2017.

[9]. RSNA Pneumonia Detection Challenge [EB/OL]. kaggle.com. /2023-01-23. https://www.kaggle.com/competitions/rsna-pneumonia-detection-challenge.

[10]. Gabruseva T, Poplavskiy D, Kalinin A. Deep learning for automatic pneumonia detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 2020: 350-351

[11]. Mooney P. Chest X-Ray Images (Pneumonia)[EB/OL]. www.kaggle.com. https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia.

[12]. Amyar A, Modzelewski R, Li H, et al. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation[J]. Computers in Biology and Medicine, 2020, 126: 104037.

[13]. Yang X, He X, Zhao J, et al. COVID-CT-dataset: a CT scan dataset about COVID-19[J]. arXiv preprint arXiv:2003.13865, 2020.

[14]. COVID-19 Lung CT Scans[EB/OL]. www.kaggle.com. https://www.kaggle.com/datasets/luisblanche/covidct.

[15]. COVID-19[EB/OL]. Medical segmentation. http://medicalsegmentation.com/covid19/.

[16]. Rajpurkar P, Irvin J, Ball R L, et al. Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists[J]. PLoS medicine, 2018, 15(11): e1002686.

[17]. Kastrama E, Pinkerton R, Samuel-Gama K. Localization of Radiographic Evidence for Pneumonia[R].

[18]. Jaiswal A K, Tiwari P, Kumar S, et al. Identifying pneumonia in chest X-rays: A deep learning approach[J]. Measurement, 2019, 145: 511-518.

[19]. Sirazitdinov I, Kholiavchenko M, Mustafaev T, et al. Deep neural network ensemble for pneumonia localization from a large-scale chest x-ray database[J]. Computers & electrical engineering, 2019, 78: 388-399.

[20]. Bhandary A, Prabhu G A, Rajinikanth V, et al. Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images[J]. Pattern Recognition Letters, 2020, 129: 271-278.

[21]. Armato S G. rd, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): A completed reference database of lung nodules on CT scans[J]. Med Phys, 2011, 38(2): 915-31.

[22]. Varshni D, Thakral K, Agarwal L, et al. Pneumonia detection using CNN based feature extraction[C]//2019 IEEE international conference on electrical, computer and communication technologies (ICECCT). IEEE, 2019: 1-7.

[23]. Kermany D S, Goldbaum M, Cai W, et al. Identifying medical diagnoses and treatable diseases by image-based deep learning[J]. cell, 2018, 172(5): 1122-1131. e9.

[24]. Kermany D S, Goldbaum M, Cai W, et al. Labeled optical coherence tomography (OCT) and chest X-ray images for classification[EB/OL]. Mendeley Data, Mendeley Data, 2018-01-06. (2018-01-06)[2023-06-20]. https://data.mendeley.com/datasets/rscbjbr9sj/2.

[25]. Meng Z, Meng L, Tomiyama H. Pneumonia diagnosis on chest X-rays with machine learning[J]. Procedia Computer Science, 2021, 187: 42-51.

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