Using Various Convolutional Neural Network to Detect Pneumonia from Chest X-Ray Images: A Systematic Literature Review

Darnell Kikoo - Bina Nusantara University Jakarta, Indonesia
Bryan Tamin - Bina Nusantara University Jakarta, Indonesia
Stephen Hardjadilaga - Bina Nusantara University Jakarta, Indonesia
- Anderies - Bina Nusantara University Jakarta, Indonesia
Irene Iswanto - Bina Nusantara University Jakarta, Indonesia

Citation Format:



Pneumonia is one of the world's top causes of mortality, especially for children. Chest X-rays serve an important part in diagnosing pneumonia due to the cost-effectiveness and quick advancement of the technology. Detecting Pneumonia through Chest X-rays (CXR) is a challenging and time-consuming process requiring trained professionals. This issue has been solved by the development of automation technology which is machine learning. Moreover, Deep Learning (DL), a machine learning specification that uses an algorithm that resembles the human brain, can predict more accurately and is now dependable enough to predict pneumonia. As time passes, another Deep Learning improvement has been made to produce a new method called Transfer Learning, that is done by extracting specific layers from some pre-trained network to be used on other datasets, which reduces the training time and improves the model performance. Although numerous algorithms are already available for pneumonia identification, a comprehensive literature evaluation and clinical recommendations are still small in numbers. This research will assist practitioners in choosing some of the best procedures from the recent research, reviewing the available datasets, and comprehending the outcomes gained in this domain. The reviewed papers show that the best score for predicting pneumonia using DL from CXR was 99.4% accuracy. The exceptional techniques and results from the reviewed papers served as great references for future research.


Pneumonia; lung diseases; X-rays; CXR; radiography; deep learning; convolutional neural network; CNN; classification

Full Text:



UNICEF, "Levels and Trends in Child Mortality Report 2017," 2017. [Online]. Available:

ATS, Top 20 pneumonia facts—2019 - american thoracic society, (accessed Mar. 21, 2022).

VOA, “Pneumonia Pembunuh Balita Nomor 2 di Indonesia.† (accessed Mar. 21, 2022).

“Pneumonia,†Mayo Clinic, (accessed Mar. 21, 2022).

K. Miller, "The 6 Different Types of Pneumonia, Explained by Doctors," 2021. (accessed Jun. 06, 2022).

X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, "ChestX-ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases," Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 3462–3471, 2017, doi: 10.1109/CVPR.2017.369.

Y. Li, Z. Zhang, C. Dai, Q. Dong, and S. Badrigilan, "Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis," Comput. Biol. Med., vol. 123, no. June, p. 103898, 2020, doi: 10.1016/j.compbiomed.2020.103898.

W. Khan, N. Zaki, and L. Ali, "Intelligent Pneumonia Identification from Chest X-Rays: A Systematic Literature Review," IEEE Access, vol. 9, pp. 51747–51771, 2021, doi: 10.1109/ACCESS.2021.3069937.

P. ran Liu, L. Lu, J. yao Zhang, T. tong Huo, S. xiang Liu, and Z. wei Ye, “Application of Artificial Intelligence in Medicine: An Overview,†Curr. Med. Sci., vol. 41, no. 6, pp. 1105–1115, 2021, doi: 10.1007/s11596-021-2474-3.

B. A. Kitchenham and S. Charters, "Guidelines for performing Systematic Literature Reviews in Software Engineering. EBSE Technical Report EBSE-2007-01. School of Computer Science and Mathematics, Keele University," no. January, pp. 1–57, 2007.

I. Lahsaini, M. El Habib Daho, and M. A. Chikh, "Convolutional Neural Network for Chest X-ray Pneumonia Detection," ACM Int. Conf. Proceeding Ser., pp. 16–20, 2020, doi: 10.1145/3432867.3432873.

Z. Jiang, "Chest X-ray Pneumonia Detection Based on Convolutional Neural Networks," Proc. - 2020 Int. Conf. Big Data, Artif. Intell. Internet Things Eng. ICBAIE 2020, pp. 341–344, 2020, doi: 10.1109/ICBAIE49996.2020.00077.

S. L. K. Yee and W. J. K. Raymond, "Pneumonia Diagnosis Using Chest X-ray Images and Machine Learning," ACM Int. Conf. Proceeding Ser., pp. 101–105, 2020, doi: 10.1145/3397391.3397412.

V. Chouhan et al., "A novel transfer learning based approach for pneumonia detection in chest X-ray images," Appl. Sci., vol. 10, no. 2, 2020, doi: 10.3390/app10020559.

G. Ali, A. Shahin, M. Elhadidi, and M. Elattar, "Convolutional Neural Network with Attention Modules for Pneumonia Detection," 2020 Int. Conf. Innov. Intell. Informatics, Comput. Technol. 3ICT 2020, pp. 0–5, 2020, doi: 10.1109/3ICT51146.2020.9311985.

S. Jamil, M. S. Abbas, Fawad, M. F. Zia, and M. U. Rahman, "A Deep Convolutional Neural Network Based Framework for Pneumonia Detection," 2021 Int. Conf. Digit. Futur. Transform. Technol. ICoDT2 2021, pp. 2–6, 2021, doi: 10.1109/ICoDT252288.2021.9441539.

I. Katsamenis, E. Protopapadakis, A. Voulodimos, A. Doulamis, and N. Doulamis, “Transfer Learning for COVID-19 Pneumonia Detection and Classification in Chest X-ray Images,†ACM Int. Conf. Proceeding Ser., pp. 170–174, 2020, doi: 10.1145/3437120.3437300.

G. Labhane, R. Pansare, S. Maheshwari, R. Tiwari, and A. Shukla, "Detection of Pediatric Pneumonia from Chest X-Ray Images using CNN and Transfer Learning," Proc. 3rd Int. Conf. Emerg. Technol. Comput. Eng. Mach. Learn. Internet Things, ICETCE 2020, no. February, pp. 85–92, 2020, doi: 10.1109/ICETCE48199.2020.9091755.

L. RaÄić, T. Popović, S. ÄŒakić, and S. Å andi, "Pneumonia Detection Using Deep Learning Based on Convolutional Neural Network," 2021 25th Int. Conf. Inf. Technol. IT 2021, no. February, pp. 17–20, 2021, doi: 10.1109/IT51528.2021.9390137.

S. A. Khoiriyah, A. Basofi, and A. Fariza, "Convolutional Neural Network for Automatic Pneumonia Detection in Chest Radiography," IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 476–480, 2020, doi: 10.1109/IES50839.2020.9231540.

A. Gawali, P. Bide, V. Kate, C. Kothastane, and E. Hirani, "Deep Learning Approach to detect Pneumonia," Proc. 4th Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2020, pp. 1277–1284, 2020, doi: 10.1109/ICECA49313.2020.9297393.

J. R. Ferreira, D. Armando Cardona Cardenas, R. A. Moreno, M. De Fatima De Sa Rebelo, J. E. Krieger, and M. Antonio Gutierrez, “Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images,†Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS, vol. 2020-July, pp. 1238–1241, 2020, doi: 10.1109/EMBC44109.2020.9176517.

R. Siddiqi, "Automated pneumonia diagnosis using a customized sequential convolutional neural network," ACM Int. Conf. Proceeding Ser., pp. 64–70, 2019, doi: 10.1145/3342999.3343001.

H. Sharma, J. S. Jain, P. Bansal, and S. Gupta, "Feature extraction and classification of chest X-ray images using CNN to detect pneumonia," Proc. Conflu. 2020 - 10th Int. Conf. Cloud Comput. Data Sci. Eng., pp. 227–231, 2020, doi: 10.1109/Confluence47617.2020.9057809.

T. A. Youssef, B. Aissam, D. Khalid, B. Imane, and J. El Miloud, "Classification of chest pneumonia from x-ray images using new architecture based on ResNet," 2020 IEEE 2nd Int. Conf. Electron. Control. Optim. Comput. Sci. ICECOCS 2020, 2020, doi: 10.1109/ICECOCS50124.2020.9314567.

D. Kermany, K. Zhang, and M. Goldbaum, "Labeled optical coherence tomography (oct) and chest x-ray images for classification," Mendeley data, vol. 2, no. 2, 2018.

A. F. Al Mubarok, J. A. M. Dominique, and A. H. Thias, "Pneumonia detection with deep convolutional architecture," Proceeding - 2019 Int. Conf. Artif. Intell. Inf. Technol. ICAIIT 2019, pp. 486–489, 2019, doi: 10.1109/ICAIIT.2019.8834476.

S. Shah, H. Mehta, and P. Sonawane, "Pneumonia detection using convolutional neural networks," Proc. 3rd Int. Conf. Smart Syst. Inven. Technol. ICSSIT 2020, no. Icssit, pp. 933–939, 2020, doi: 10.1109/ICSSIT48917.2020.9214289.

L. Mao, T. Yumeng, and C. Lina, "Pneumonia Detection in chest X-rays: A deep learning approach based on ensemble RetinaNet and Mask R-CNN," Proc. - 2020 8th Int. Conf. Adv. Cloud Big Data, CBD 2020, pp. 213–218, 2020, doi: 10.1109/CBD51900.2020.00046.

H. Ko, H. Ha, H. Cho, K. Seo, and J. Lee, "Pneumonia Detection with Weighted Voting Ensemble of CNN Models," 2019 2nd Int. Conf. Artif. Intell. Big Data, ICAIBD 2019, pp. 306–310, 2019, doi: 10.1109/ICAIBD.2019.8837042.

Z. Wang, J. Hall, and R. J. Haddad, "Improving pneumonia diagnosis accuracy via systematic convolutional neural network-based image enhancement," Conf. Proc. - IEEE SOUTHEASTCON, vol. 2021-March, pp. 0–5, 2021, doi: 10.1109/SoutheastCon45413.2021.9401810.

A. Tilve, S. Nayak, S. Vernekar, D. Turi, P. R. Shetgaonkar, and S. Aswale, "Pneumonia Detection Using Deep Learning Approaches," Int. Conf. Emerg. Trends Inf. Technol. Eng. ic-ETITE 2020, pp. 1–8, 2020, doi: 10.1109/ic-ETITE47903.2020.152.

D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, "Pneumonia Detection Using CNN based Feature Extraction," Proc. 2019 3rd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2019, pp. 1–7, 2019, doi: 10.1109/ICECCT.2019.8869364.

M. Aledhari, S. Joji, M. Hefeida, and F. Saeed, "Optimized CNN-based Diagnosis System to Detect the Pneumonia from Chest Radiographs," Proc. - 2019 IEEE Int. Conf. Bioinforma. Biomed. BIBM 2019, pp. 2405–2412, 2019, doi: 10.1109/BIBM47256.2019.8983114.

D. Hirahara, E. Yuda, T. Takahara, and Y. Kobayashi, "Fundamental study on preliminary image processing at time development of CNN using chest radiography," 2019 IEEE 1st Glob. Conf. Life Sci. Technol. LifeTech 2019, pp. 102–104, 2019, doi: 10.1109/LifeTech.2019.8884064.

X. Gu, L. Pan, H. Liang, and R. Yang, "Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography," ACM Int. Conf. Proceeding Ser., pp. 88–93, 2018, doi: 10.1145/3195588.3195597.

P. Naveen and B. Diwan, "Pre-trained VGG-16 with CNN architecture to classify X-Rays images into normal or pneumonia," 2021 Int. Conf. Emerg. Smart Comput. Informatics, ESCI 2021, pp. 102–105, 2021, doi: 10.1109/ESCI50559.2021.9396997.

D. Demner-Fushman et al., "Preparing a collection of radiology examinations for distribution and retrieval," J. Am. Med. Informatics Assoc., vol. 23, no. 2, pp. 304–310, 2016, doi: 10.1093/jamia/ocv080.

A. Panwar, A. Dagar, V. Pal, and V. Kumar, "COVID 19, pneumonia and other disease classification using chest X-ray images," 2021 2nd Int. Conf. Emerg. Technol. INCET 2021, pp. 23–26, 2021, doi: 10.1109/INCET51464.2021.9456192.

Joseph Paul Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, "COVID-19 Image Data Collection: Prospective Predictions Are the Future," 2020, [Online]. Available:

J. R. Zech, M. A. Badgeley, M. Liu, A. B. Costa, J. J. Titano, and E. K. Oermann, "Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study," PLoS Med., vol. 15, no. 11, pp. 1–17, 2018, doi: 10.1371/journal.pmed.1002683.

S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, "Two public chest X-ray datasets for computer-aided screening of pulmonary diseases.," Quant. Imaging Med. Surg., vol. 4, no. 6, pp. 475–7, 2014, doi: 10.3978/j.issn.2223-4292.2014.11.20.