Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Mahmud Suyuti, Endang Setyati


The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.


pneumonia; pneumonia classification; thorax image; CNN

Full Text:



E. Puddy and C. Hill, “Interpretation of the chest radiograph,” Contin. Educ. Anaesthesia, Crit. Care Pain, vol. 7, no. 3, pp. 71–75, 2007, doi: 10.1093/bjaceaccp/mkm014.

B. Kelly, “The chest radiograph,” Ulster Med. J., vol. 81, no. 3, pp. 143–148, 2012, doi: 10.1016/b978-0-323-39952-4.00004-4.

N. Singh, “Wavelet Transform Based Pneumonia Classification of Chest X- Ray Images,” 2019 Int. Conf. Comput. Power Commun. Technol., pp. 540–545, 2019.

A. Sharma, D. Raju, and S. Ranjan, “Detection of pneumonia clouds in chest X-ray using image processing approach,” 2017 Nirma Univ. Int. Conf. Eng. NUiCONE 2017, vol. 2018-Janua, pp. 1–4, 2018, doi: 10.1109/NUICONE.2017.8325607.

B. Li, G. Kang, K. Cheng, and N. Zhang, “Attention-Guided Convolutional Neural Network for Detecting Pneumonia on Chest X-Rays,” 2019 41st Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 4851–4854, 2019, doi: 10.1109/embc.2019.8857277.

S. Sedai, D. Mahapatra, Z. Ge, R. Chakravorty, and R. Garnavi, Deep multiscale convolutional feature learning for weakly supervised localization of chest pathologies in X-ray images, vol. 11046 LNCS. Springer, 2018.

S. Albawi, T. A. Mohammed, and S. Al-Zawi, “Understanding of a convolutional neural network,” Proc. 2017 Int. Conf. Eng. Technol. ICET 2017, vol. 2018-Janua, no. August, pp. 1–6, 2018, doi: 10.1109/ICEngTechnol.2017.8308186.

S. J. Heo et al., “Deep learning algorithms with demographic information help to detect tuberculosis in chest radiographs in annual workers’ health examination data,” Int. J. Environ. Res. Public Health, vol. 16, no. 2, 2019, doi: 10.3390/ijerph16020250.

S. Defiyanti, “Integrasi metode klasifikasi dan clustering dalam data mining,” 2017, pp. 33–34.

X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “ChestX-ray: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly Supervised Classification and Localization of Common Thorax Diseases,” Adv. Comput. Vis. Pattern Recognit., pp. 369–392, 2019, doi: 10.1007/978-3-030-13969-8_18.

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.

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.

V. Srinivasan, A. R. Sankar, and V. N. Balasubramanian, “ADINE: An adaptive momentum method for stochastic gradient descent,” ACM Int. Conf. Proceeding Ser., no. December 2017, pp. 249–256, 2018, doi: 10.1145/3152494.3152515.

K. H. Mahmud, Adiwijaya, and S. Al Faraby, “Klasifikasi Citra Multi-Kelas Menggunakan Convolutional Neural Network,” e-Proceeding Eng., vol. 6, no. 1, pp. 2127–2136, 2019.

R. Poojary and A. Pai, “Comparative Study of Model Optimization Techniques in Fine-Tuned CNN Models,” in 2019 International Conference on Electrical and Computing Technologies and Applications (ICECTA), 2019, pp. 1–4, doi: 10.1109/ICECTA48151.2019.8959681.



  • There are currently no refbacks.

Copyright (c) 2020 Mahmud Suyuti

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

INFORM: Jurnal Ilmiah Bidang Teknologi Informasi dan Komunikasi
ISSN 2502-3470 (Print) | 2581-0367 (Online)
Published By Universitas Dr. Soetomo
Managed By Program Studi Teknik Informatika, Fakultas Teknik, Universitas Dr. Soetomo
Address Jl. Semolowaru no 84, Surabaya, 60118, (031) 5944744
email [email protected]

Inform is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View Inform Stats

Inform is supervised by Relawan Jurnal Indonesia.