Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia

Enes Ayan

doi:10.35377/saucis.5.69696.1019187

EN

Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia

Abstract

Pneumonia is a general public health problem. It is an important risk factor, especially for children under 5 years old and people aged 65 and older. Fortunately, it is a treatable disease when diagnosed in the early phase. The most common diagnostic method known for the disease is chest X-Rays. However, the disease can be confused with different disorders in the lungs or its variants by experts. In this context, computer-aided diagnostic systems are necessary to provide a second opinion to experts. Convolutional neural networks are a subfield in deep learning and they have demonstrated success in solving many medical problems. In this paper, Xception which is a convolutional neural network was trained with the transfer learning method to detect viral pneumonia, normal cases, and bacterial pneumonia in chest X-Rays. Then, five different machine learning classification algorithms were trained with the features obtained by the trained convolutional neural network. The classification performances of the algorithms were compared. According to the test results, Xception achieved the best classification result with an accuracy of 89.74%. On the other hand, SVM achieved the closest classification performance to the convolutional neural network model with 89.58% accuracy.

Keywords

References

[1] D. You, G. Jones, and T. Wardlaw, "Levels & Trends in Child Mortality: Report 2011. Estimates Developed by the UN Inter-Agency Group for Child Mortality Estimation.," New York: United Nations Children’s Fund2011.
[2] WHO, "Priority diseases and reasons for inclusion," in Chapter 6.22-Pneumonia, 2014.
[3] O. Ruuskanen, E. Lahti, L. C. Jennings, and D. R. Murdoch, "Viral pneumonia," The Lancet, vol. 377, no. 9773, pp. 1264-1275, 2011.
[4] D. E. Drake, A. Cohen, and J. Cohn, "National hospital antibiotic timing measures for pneumonia and antibiotic overuse," Quality Management in Healthcare, vol. 16, no. 2, pp. 113-122, 2007.
[5] WHO, "Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children," Geneva: World Health Organization2001.
[6] M. I. Neuman et al., "Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children," Journal of Hospital Medicine, vol. 7, no. 4, pp. 294-298, 2012.
[7] J. Ker, L. Wang, J. Rao, and T. Lim, "Deep learning applications in medical image analysis," IEEE Access, vol. 6, pp. 9375-9389, 2017.
[8] D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual Review of Biomedical Engineering, vol. 19, pp. 221-248, 2017.

[9] M. A. Mazurowski, M. Buda, A. Saha, and M. R. Bashir, "Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI," Journal of Magnetic Resonance Imaging, vol. 49, no. 4, pp. 939-954, 2019.
[10] Y. LeCun, K. Kavukcuoglu, and C. Farabet, "Convolutional networks and applications in vision," in Proceedings of 2010 IEEE international symposium on circuits and systems, 2010, pp. 253-256: IEEE.
[11] M. A. Al-Antari, M. A. Al-Masni, and T.-S. Kim, "Deep learning computer-aided diagnosis for breast lesion in digital mammogram," Deep Learning in Medical Image Analysis, pp. 59-72, 2020.
[12] H. Li, A. Li, and M. Wang, "A novel end-to-end brain tumor segmentation method using improved fully convolutional networks," Computers in biology medicine, vol. 108, pp. 150-160, 2019. [13] A. Esteva et al., "Dermatologist-level classification of skin cancer with deep neural networks," Nature, vol. 542, no. 7639, p. 115, 2017.
[14] P. Rajpurkar et al., "Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning," arXiv preprint arXiv:1711.05225, 2017. [15] D. S. Kermany et al., "Identifying medical diagnoses and treatable diseases by image-based deep learning," Cell, vol. 172, no. 5, pp. 1122-1131. e9, 2018.
[16] G. Liang and L. Zheng, "A transfer learning method with deep residual network for pediatric pneumonia diagnosis," Computer Methods and Programs in Biomedicine, p. 104964, 2019.
[17] V. Chouhan et al., "A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images," Applied Sciences, vol. 10, no. 2, p. 559, 2020.
[18] X. Gu, L. Pan, H. Liang, and R. Yang, "Classification of bacterial and viral childhood pneumonia using deep learning in chest radiography," in Proceedings of the 3rd International Conference on Multimedia and Image Processing, 2018, pp. 88-93.
[19] A. Mittal et al., "Detecting Pneumonia using Convolutions and Dynamic Capsule Routing for Chest X-ray Images," Sensors, vol. 20, no. 4, p. 1068, 2020.
[20] K. A. Prayogo, A. Suryadibrata, and J. C. Young, "Classification of pneumonia from X-ray images using siamese convolutional network," Telkomnika, vol. 18, no. 3, pp. 1302-1309, 2020.
[21] T. Rahman et al., "Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray," Applied Sciences, vol. 10, no. 9, p. 3233, 2020.
[22] M. F. Hashmi, S. Katiyar, A. G. Keskar, N. D. Bokde, and Z. W. Geem, "Efficient pneumonia detection in chest xray images using deep transfer learning," Diagnostics, vol. 10, no. 6, p. 417, 2020.
[23] T. Mahmud, M. A. Rahman, and S. A. Fattah, "CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization," Computers in biology medicine, vol. 122, p. 103869, 2020. [24] K. El Asnaoui, "Design ensemble deep learning model for pneumonia disease classification," International Journal of Multimedia Information Retrieval, vol. 10, no. 1, pp. 55-68, 2021.
[25] M. B. Darici, Z. Dokur, and T. Olmez, "Pneumonia Detection and Classification Using Deep Learning on Chest X-Ray Images," International Journal of Intelligent Systems Applications in Engineering, vol. 8, no. 4, pp. 177-183, 2020. [26] D. Kermany and M. Goldbaum, "Labeled optical coherence tomography (OCT) and Chest X-Ray images for classification," Mendeley Data, vol. 2, 2018.
[27] J. Koushik, "Understanding convolutional neural networks," arXiv preprint arXiv:1605.09081, 2016.
[28] F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017, pp. 1251-1258. [29] K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," Journal of Big Data, vol. 3, no. 1, p. 9, 2016.
[30] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248-255: IEEE.
[31] W. Rawat and Z. Wang, "Deep convolutional neural networks for image classification: A comprehensive review," Neural Computation, vol. 29, no. 9, pp. 2352-2449, 2017.
[32] C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of Big Data, vol. 6, no. 1, p. 60, 2019.
[33] E. Ayan and H. M. Ünver, "Data augmentation importance for classification of skin lesions via deep learning," in Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT), 2018, pp. 1-4: IEEE.
[34] G. Bonaccorso, Machine learning algorithms. Packt Publishing Ltd, 2017.

Details

Primary Language

English

Subjects

Artificial Intelligence , Computer Software , Software Engineering (Other)

Journal Section

Research Article

Authors

Enes Ayan ^*
0000-0002-5463-8064
Türkiye

Publication Date

April 30, 2022

Submission Date

November 4, 2021

Acceptance Date

March 1, 2022

Published in Issue

Year 1970 Volume: 5 Number: 1

DOI

https://doi.org/10.35377/saucis.5.69696.1019187

IZ

https://izlik.org/JA75RH22BX

Cite

RIS / Bibtex

APA

Ayan, E. (2022). Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia. Sakarya University Journal of Computer and Information Sciences, 5(1), 48-61. https://doi.org/10.35377/saucis.5.69696.1019187

AMA

1.Ayan E. Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia. SAUCIS. 2022;5(1):48-61. doi:10.35377/saucis.5.69696.1019187

Chicago

Ayan, Enes. 2022. “Using a Convolutional Neural Network As Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia”. Sakarya University Journal of Computer and Information Sciences 5 (1): 48-61. https://doi.org/10.35377/saucis.5.69696.1019187.

EndNote

Ayan E (April 1, 2022) Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia. Sakarya University Journal of Computer and Information Sciences 5 1 48–61.

IEEE

[1]E. Ayan, “Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia”, SAUCIS, vol. 5, no. 1, pp. 48–61, Apr. 2022, doi: 10.35377/saucis.5.69696.1019187.

ISNAD

Ayan, Enes. “Using a Convolutional Neural Network As Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia”. Sakarya University Journal of Computer and Information Sciences 5/1 (April 1, 2022): 48-61. https://doi.org/10.35377/saucis.5.69696.1019187.

JAMA

1.Ayan E. Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia. SAUCIS. 2022;5:48–61.

MLA

Ayan, Enes. “Using a Convolutional Neural Network As Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia”. Sakarya University Journal of Computer and Information Sciences, vol. 5, no. 1, Apr. 2022, pp. 48-61, doi:10.35377/saucis.5.69696.1019187.

Vancouver

1.Enes Ayan. Using a Convolutional Neural Network as Feature Extractor for Different Machine Learning Classifiers to Diagnose Pneumonia. SAUCIS. 2022 Apr. 1;5(1):48-61. doi:10.35377/saucis.5.69696.1019187