Deep Learning for Pneumonia Detection in Chest X-Rays using Different Algorithms and Transfer Learning Architectures

Danur Lestari; Anggi Mulya; Aghnia Tatamara; Ryando Rama Haiban; Habibah Dian Khalifah

doi:10.57152/predatecs.v3i1.1553

Authors

Danur Lestari Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Anggi Mulya Universitas Islam Negeri Sultan Syarif Kasim Riau, Indonesia
Aghnia Tatamara Dokuz Eylul University, Turkey
Ryando Rama Haiban Dokuz Eylul University, Turkey
Habibah Dian Khalifah Yarmouk University, Jordan

DOI:

https://doi.org/10.57152/predatecs.v3i1.1553

Keywords:

Convolutional Neural Network, Generative Adversarial Network, InceptionV3, Pneumonia, ResNet50V2

Abstract

Pneumonia is one of the lung conditions brought on by bacterial infections. An accurate diagnosis is necessary for successful treatment. A radiologist can typically diagnose the condition based on images from a chest X-ray. The diagnosis may be arbitrary for a variety of reasons, such as the indistinctness of certain diseases on chest X-ray pictures or the possibility of the illness being mistaken for another. Consequently, clinicians require guidance from computer-aided diagnosis tools. We diagnosed pneumonia using two algorithms CNN and GAN, as well as two architectures ResNet50V2 and InceptionV3. The test results show that the ResNet50V2 architecture is superior to the InceptionV3 architecture on the CNN algorithm with an accuracy of 94% versus 93%. In addition, the test results on the GANs algorithm show that the ResNet50V2 architecture is superior to the InceptionV3 architecture with an accuracy of 96%, while the InceptionV3 architecture achieves an accuracy of 92%.

References

M. M. Hasan, M. M. J. Kabir, M. R. Haque, and M. Ahmed, “A combined approach using image processing and deep learning to detect pneumonia from chest X-ray image,” in 2019 3rd international conference on electrical, Computer & Telecommunication Engineering (ICECTE), IEEE, 2019, pp. 89–92.

M. Aydogdu, E. Ozyilmaz, H. Aksoy, G. Gursel, and N. Ekim, “Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores,” Tuberk Toraks, vol. 58, no. 1, pp. 25–34, 2010.

B. PRUITT, “Pneumonia: Etiology, Care and Prevention.,” RT: The Journal for Respiratory Care Practitioners, vol. 37, no. 4, 2024.

Q. An, W. Chen, and W. Shao, “A Deep Convolutional Neural Network for Pneumonia Detection in X-ray Images with Attention Ensemble,” Diagnostics, vol. 14, no. 4, p. 390, 2024.

M. I. Neuman et al., “Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children,” J Hosp Med, vol. 7, no. 4, pp. 294–298, 2012.

R. M. Hopstaken, T. Witbraad, J. M. A. Van Engelshoven, and G. J. Dinant, “Inter-observer variation in the interpretation of chest radiographs for pneumonia in community-acquired lower respiratory tract infections,” Clin Radiol, vol. 59, no. 8, pp. 743–752, 2004.

W. H. Organization, “Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children,” World Health Organization, 2001.

D. Hastari, S. Winanda, A. R. Pratama, N. Nurhaliza, and E. S. Ginting, “Application of Convolutional Neural Network ResNet-50 V2 on Image Classification of Rice Plant Disease,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, 2024.

D. Srivastav, A. Bajpai, and P. Srivastava, “Improved classification for pneumonia detection using transfer learning with GAN based synthetic image augmentation,” in 2021 11th international conference on cloud computing, data science & engineering (confluence), IEEE, 2021, pp. 433–437.

P. D. Rinanda, D. N. Aini, T. A. Pertiwi, S. Suryani, and A. J. Prakash, “Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach,” Public Research Journal of Engineering, Data Technology and Computer Science, vol. 1, no. 2, pp. 56–61, 2024.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Adv Neural Inf Process Syst, vol. 25, 2012.

V. Sirish Kaushik, A. Nayyar, G. Kataria, and R. Jain, “Pneumonia detection using convolutional neural networks (CNNs),” in Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), Springer, 2020, pp. 471–483.

A. Madhavi, M. S. Abhijna, P. Sumanjali, S. Supraja, M. Ghalwan, and M. R. Chary, “Automated Diagnosis of Pneumonia Using CNN and Transfer Learning Approaches,” in E3S Web of Conferences, EDP Sciences, 2023, p. 01031.

X. Liu and C.-J. Hsieh, “Rob-gan: Generator, discriminator, and adversarial attacker,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 11234–11243.

S. Vashisht, S. Lamba, B. Sharma, and A. Sharma, “Pneumonia classification using CNN-GAN,” in 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), IEEE, 2023, pp. 456–461.

A. U. Ibrahim, M. Ozsoz, S. Serte, F. Al-Turjman, and P. S. Yakoi, “Pneumonia classification using deep learning from chest X-ray images during COVID-19,” Cognit Comput, vol. 16, no. 4, pp. 1589–1601, 2024.

E. Ayan and H. M. Ünver, “Diagnosis of pneumonia from chest X-ray images using deep learning,” in 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT), Ieee, 2019, pp. 1–5.

P. Rajpurkar, “CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning,” ArXiv abs/1711, vol. 5225, 2017.

D. Kermany, “Labeled optical coherence tomography (oct) and chest x-ray images for classification,” Mendeley data, 2018.

K. Kallianos et al., “How far have we come? Artificial intelligence for chest radiograph interpretation,” Clin Radiol, vol. 74, no. 5, pp. 338–345, 2019.

N. A. Andriyanov, “Analysis of the acceleration of neural networks inference on intel processors based on openvino toolkit,” in 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), IEEE, 2020, pp. 1–5.

C. Bailer, T. Habtegebrial, and D. Stricker, “Fast feature extraction with CNNs with pooling layers,” arXiv preprint arXiv:1805.03096, 2018.

L. Engstrom, B. Tran, D. Tsipras, L. Schmidt, and A. Madry, “A rotation and a translation suffice: Fooling cnns with simple transformations,” 2017.

S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” in International conference on machine learning, PMLR, 2016, pp. 1060–1069.

C. Ledig et al., “Photo-realistic single image super-resolution using a generative adversarial network,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4681–4690.

P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1125–1134.

H. Wu, S. Zheng, J. Zhang, and K. Huang, “Gp-gan: Towards realistic high-resolution image blending,” in Proceedings of the 27th ACM international conference on multimedia, 2019, pp. 2487–2495.

R. A. Yeh, C. Chen, T. Yian Lim, A. G. Schwing, M. Hasegawa-Johnson, and M. N. Do, “Semantic image inpainting with deep generative models,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5485–5493.

I. Goodfellow et al., “Generative adversarial nets,” Adv Neural Inf Process Syst, vol. 27, 2014.

A. K. Jaiswal, P. Tiwari, S. Kumar, D. Gupta, A. Khanna, and J. J. P. C. Rodrigues, “Identifying pneumonia in chest X-rays: A deep learning approach,” Measurement, vol. 145, pp. 511–518, 2019.

K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, Springer, 2016, pp. 630–645.

S. S. S. Koushik and K. G. Srinivasa, “Detection of respiratory diseases from chest X rays using Nesterov accelerated adaptive moment estimation,” Measurement, vol. 176, p. 109153, 2021.