DETECTION OF PNEUMONIA FROM X-RAY IMAGES USING DEEP LEARNING TECHNIQUES

Abstract

X-ray images is one of the most common utilities used by health care specialists for detecting healthy problems in patients’ chest. In this work, deep learning techniques have been adopted for diagnosing and detecting of lung diseases. First, an experimental study has been conducted for selecting the best artificial neural network ANN model that can be used for lung X-Ray image classification. The obtained best model has been used for classifying the lung X-Ray images into three classes (Multi class classification) namely bacterial pneumonia, viral pneumonia, and healthy lung. After that, three well-known CNN architectures, namely ResNet, Inception, and MobileNet have been adopted and used as a feature extractor for the selected best ANN model. Moreover, the above-mentioned ANN model (both with and without the features extraction phase) has been used for classifying the lung X-Ray images as healthy and pneumonia lungs (Binary classification). As a result of the study, the proposed ANN model with ResNet feature extraction phase gave the highest classification accuracy rate of 81.67% when multi-class classification has been conducted on the lung X-Ray dataset. On the other hand, the proposed ANN model with MobileNet feature extraction phase gave the highest accuracy rate of 95.67% when a binary classification has been conducted on the X-Ray image dataset.

Keywords

• X-Ray
• Artificial Neural Networks
• Pneumonia; Pre-Trained Models
• Deep Learning

References

1. [1] Ayan, R. and Ünver, H. M., (2019), “Diagnosis of pneumonia from chest X-ray images using deep learning,” in 2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), pp. 1–5.
2. [2] Rajpurkar et al., (2017), “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,” arXiv preprint arXiv:1711.05225.
3. [3] El Asnaoui, K.,Chawki, Y. and Idri, A., (2021), “Automated methods for detection and classification pneumonia based on x-ray images using deep learning,” in Artificial intelligence and blockchain for future cybersecurity applications, Springer, pp. 257–284.
4. [4] Jain, R.., Nagrath, R., Kataria, G., Kaushik, V. S. and Hemanth, D. J., (2020), “Pneumonia detection in chest X-ray images using convolutional neural networks and transfer learning,” Measurement, vol. 165, p. 108046.
5. [5] Hasan et al., (2021), “Deep learning approaches for detecting pneumonia in COVID-19 patients by analyzing chest X-ray images,” Math Probl Eng, vol. 2021, pp. 1–8.
6. [6] Stephen, O., Sain, M., Maduh, U. J. and Jeong, D-U., (2019), “An efficient deep learning approach to pneumonia classification in healthcare,” J Healthc Eng, vol. 2019.
7. [7] Elshennawy, N.M. and Ibrahim, D. M., (2020), “Deep-pneumonia framework using deep learning models based on chest X-ray images,” Diagnostics, vol. 10, no. 9, p. 649.
8. [8] Jaiswal, A. K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A. and Rodrigues, J. J. P. C., (2019) “Identifying pneumonia in chest X-rays: A deep learning approach,” Measurement, vol. 145, pp. 511–518.

9. [9] El Asnaoui, K. and Chawki, Y., (2021), “Using X-ray images and deep learning for automated detection of coronavirus disease,” J Biomol Struct Dyn, vol. 39, no. 10, pp. 3615–3626.
10. [10] Ouchicha, C., Ammor, O. and Meknassi, M., (2020), “CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images,” Chaos Solitons Fractals, vol. 140, p. 110245.
11. [11] Narin, A., Kaya, C., and Pamuk, Z., (2021), “Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks,” Pattern Analysis and Applications, vol. 24, pp. 1207–1220.
12. [12] Ozturk, T., Talo, M., Yildirim, E. A., Baloglu, U. B., Yildirim, O. and Acharya, U. R., (2020), “Automated detection of COVID-19 cases using deep neural networks with X-ray images,” Comput Biol Med, vol. 121, p. 103792.
13. [13] Jain, G., Mittal, D., Thakur, D. and Mittal, M. K., (2020), “A deep learning approach to detect Covid-19 coronavirus with X-Ray images,” Biocybern Biomed Eng, vol. 40, no. 4, pp. 1391–1405.
14. [14] Darici, M. B., Dokur, Z. and Olmez, T., (2020) “Pneumonia detection and classification using deep learning on chest x-ray images,” International Journal of Intelligent Systems and Applications in Engineering, vol. 8, no. 4, pp. 177–183.
15. [15] Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V. and Pachori, R. B., (2021), “Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study,” Biomed Signal Process Control, vol. 64, p. 102365.
16. [16] Apostolopoulos, I. D., Aznaouridis, S. I. and Tzani, M. A., (2020), “Extracting possibly representative COVID-19 biomarkers from X-ray images with deep learning approach and image data related to pulmonary diseases,” J Med Biol Eng, vol. 40, pp. 462–469.
17. [17] Bhardwaj P. and Kaur, A., (2021), “A novel and efficient deep learning approach for COVID‐19 detection using X‐ray imaging modality,” Int J Imaging Syst Technol, vol. 31, no. 4, pp. 1775–1791.
18. [18] Bakır, H. and Yılmaz, Ş., “Using Transfer Learning Technique as a Feature Extraction Phase for Diagnosis of Cataract Disease in the Eye,” International Journal of Sivas University of Science and Technology, vol. 1, no. 1, pp. 17–33.
19. [19] Kermany et al., (2018), “Identifying medical diagnoses and treatable diseases by image-based deep learning,” Cell, vol. 172, no. 5, pp. 1122–1131.
20. [20] Khobragade, S., Tiwari, A., Patil, C. Y., and Narke, V., (2016), “Automatic detection of major lung diseases using chest radiographs and classification by feed-forward artificial neural network,” in 2016 IEEE 1st international conference on power electronics, intelligent control and energy systems (ICPEICES), pp. 1–5.
21. [21] Hassaballah M., and Awad, A. I., (2020), Deep learning in computer vision: principles and applications. CRC Press.
22. [22] Otter, D. W., Medina, J. R. and Kalita, J. K., (2020), “A survey of the usages of deep learning for natural language processing,” IEEE Trans Neural Netw Learn Syst, vol. 32, no. 2, pp. 604–624.
23. [23] He, K., Zhang, X., Ren, S. and Sun, J., (2016) “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778.
24. [24] Szegedy et al., (2015), “Going deeper with convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9.
25. [25] Howard et al., (2019), “Searching for mobilenetv3,” in Proceedings of the IEEE/CVF international conference on computer vision, pp. 1314–1324.
26. [26] Sirish Kaushik, V., Nayyar, A., Kataria, G. and Jain, R., (2020), “Pneumonia detection using convolutional neural networks (CNNs),” in Proceedings of First International Conference on Computing, Communications, and Cyber-Security (IC4S 2019), pp. 471–483.
27. [27] Sharma, H. , Jain, J. S., Bansal, P., and Gupta, S., (2020), “Feature extraction and classification of chest x-ray images using cnn to detect pneumonia,” in 2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), pp. 227–231.
28. [28] Al Mubarok, A. F., Dominique, J. A. M. and Thias, A. H., (2019), “Pneumonia detection with deep convolutional architecture,” in 2019 International conference of artificial intelligence and information technology (ICAIIT), pp. 486–489.