Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.
| Published in | American Journal of Computer Science and Technology (Volume 5, Issue 2) |
| DOI | 10.11648/j.ajcst.20220502.11 |
| Page(s) | 26-33 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Deep Learning, Convolutional Neural Network, Transfer Learning, Support Vector Machine, Chest X-ray, Pneumonia
| [1] | Saul, C. J., Urey, D. Y. & Taktakoglu, C. D. (2019). Early diagnosis of pneumonia with deep learning. arXiv preprint arXiv: 1904.00937. |
| [2] | Skolnik, R., 2016. Global Health 101, 3. Izd. |
| [3] | Aydogdu, M., Ozyilmaz, E., Aksoy, H., Gursel, G. & Ekim, N. (2010). Mortality prediction in community-acquired pneumonia requiring mechanical ventilation; values of pneumonia and intensive care unit severity scores. Tuberk Toraks, 58 (1), pp. 25-34. |
| [4] | World Health Organization (2001). Standardization of interpretation of chest radiographs for the diagnosis of pneumonia in children (No. WHO/V&B/01.35). World Health Organization. |
| [5] | Gang, P., Zhen, W., Zeng, W., Gordienko, Y., Kochura, Y., Alienin, O., Rokovyi, O. & Stirenko, S. (2018), March. Dimensionality reduction in deep learning for chest X-ray analysis of lung cancer. In 2018 tenth international conference on advanced computational intelligence (ICACI) (pp. 878-883). IEEE. |
| [6] | Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C. & Lindseth, F. (2015). Medical image segmentation on GPUs–A comprehensive review. Medical image analysis, 20 (1), pp. 1-18. |
| [7] | LeCun, Y., Bengio, Y. & Hinton, G. (2015). Deep learning. nature, 521 (7553), pp. 436-444. |
| [8] | Rajpurkar, P., Irvin, J., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C., Shpanskaya, K. & Lungren, M. P. (2017). Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning. arXiv preprint arXiv: 1711.05225. |
| [9] | Shin, H. C., Roberts, K., Lu, L., Demner-Fushman, D., Yao, J. & Summers, R. M. (2016). Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2497-2506). |
| [10] | Avendi, M. R., Kheradvar, A. & Jafarkhani, H. (2016). A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Medical image analysis, 30, pp. 108-119. |
| [11] | Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S. & Navab, N. (2016). Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE transactions on medical imaging, 35 (5), pp. 1313-1321. |
| [12] | Stephen, O., Sain, M., Maduh, U. J. & Jeong, D. U. (2019). An efficient deep learning approach to pneumonia classification in healthcare. Journal of healthcare engineering, 2019. |
| [13] | Chouhan, V., Singh, S. K., Khamparia, A., Gupta, D., Tiwari, P., Moreira, C., Damaševičius, R. & De Albuquerque, V. H. C. (2020). A novel transfer learning based approach for pneumonia detection in chest X-ray images. Applied Sciences, 10 (2), p. 559. |
| [14] | Bennur, A. (2019). Ensemble Model for X-ray Image Classification (Doctoral dissertation, Dublin, National College of Ireland). |
| [15] | Rahman, T., Chowdhury, M. E., Khandakar, A., Islam, K. R., Islam, K. F., Mahbub, Z. B., Kadir, M. A. & Kashem, S. (2020). Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray. Applied Sciences, 10 (9), p. 3233. |
| [16] | Ayan, E. & 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). Ieee. |
| [17] | Rajaraman, S., Candemir, S., Kim, I., Thoma, G. & Antani, S. (2018). Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Applied Sciences, 8 (10), p. 1715. |
| [18] | Mooney, P., Chest X-Ray Images (Pneumonia) (2020). https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. |
| [19] | He, K., Zhang, X., Ren, S. & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). |
| [20] | Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D.,... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). |
| [21] | Jain, A., Mehra, N., & Khare, P. (2021). A Survey on Pneumonia Detection Methods Using Computer-aided Diagnosis. International Journal, 9 (7). |
| [22] | Tilve, A., Nayak, S., Vernekar, S., Turi, D., Shetgaonkar, P. R., & Aswale, S. (2020, February). Pneumonia detection using deep learning approaches. In 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE) (pp. 1-8). IEEE. |
| [23] | Yee, S. L. K., & Raymond, W. J. K. (2020, September). Pneumonia diagnosis using chest x-ray images and machine learning. In Proceedings of the 2020 10th International Conference on Biomedical Engineering and Technology (pp. 101-105). |
| [24] | Thanh, H. T., Yen, P. H., & Ngoc, T. B. (2021, March). Pneumonia Classification in X-ray Images Using Artificial Intelligence Technology. In 2020 Applying New Technology in Green Buildings (ATiGB) (pp. 25-30). IEEE. |
APA Style
Alhussein Mohammed Ahmed, Gais Alhadi Babikir, Salma Mohammed Osman. (2022). Classification of Pneumonia Using Deep Convolutional Neural Network. American Journal of Computer Science and Technology, 5(2), 26-33. https://doi.org/10.11648/j.ajcst.20220502.11
ACS Style
Alhussein Mohammed Ahmed; Gais Alhadi Babikir; Salma Mohammed Osman. Classification of Pneumonia Using Deep Convolutional Neural Network. Am. J. Comput. Sci. Technol. 2022, 5(2), 26-33. doi: 10.11648/j.ajcst.20220502.11
AMA Style
Alhussein Mohammed Ahmed, Gais Alhadi Babikir, Salma Mohammed Osman. Classification of Pneumonia Using Deep Convolutional Neural Network. Am J Comput Sci Technol. 2022;5(2):26-33. doi: 10.11648/j.ajcst.20220502.11
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Download@article{10.11648/j.ajcst.20220502.11,
author = {Alhussein Mohammed Ahmed and Gais Alhadi Babikir and Salma Mohammed Osman},
title = {Classification of Pneumonia Using Deep Convolutional Neural Network},
journal = {American Journal of Computer Science and Technology},
volume = {5},
number = {2},
pages = {26-33},
doi = {10.11648/j.ajcst.20220502.11},
url = {https://doi.org/10.11648/j.ajcst.20220502.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20220502.11},
abstract = {Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively.},
year = {2022}
}
TY - JOUR T1 - Classification of Pneumonia Using Deep Convolutional Neural Network AU - Alhussein Mohammed Ahmed AU - Gais Alhadi Babikir AU - Salma Mohammed Osman Y1 - 2022/04/14 PY - 2022 N1 - https://doi.org/10.11648/j.ajcst.20220502.11 DO - 10.11648/j.ajcst.20220502.11 T2 - American Journal of Computer Science and Technology JF - American Journal of Computer Science and Technology JO - American Journal of Computer Science and Technology SP - 26 EP - 33 PB - Science Publishing Group SN - 2640-012X UR - https://doi.org/10.11648/j.ajcst.20220502.11 AB - Pneumonia is considered a serious and fatal disease worldwide. In fact, pneumonia can be an individual's life-endangering if not treated promptly by drugs. Therefore, the early detection of pneumonia enhances the chances of recovery, which helps reduce mortality. It is worth noting that X-rays are one of the most important diagnostic tools for diagnosing pneumonia. In fact, Chest X-ray is widely used in the diagnosis of many lung diseases (such as: Breast Cancer, Pneumonia, Tuberculosis, etc.), due to lower diagnostic costs. Indeed, the diagnoses can be subjective for many reasons for example the appearance of disease which can be unclear in chest X-ray images or can be confused with other diseases. Hence, for enhancing the level of diagnosis to guide clinicians, computer-aided diagnosis systems will be needed. In this paper, we put forward to develop a structure to classify pneumonia from chest X-ray images using a Convolutional Neural Network (CNN) and residual network architecture. Clearly, to determine if a person is infected with pneumonia or not, we used two well-known CNN pre-trained models (ResNet50 and ResNet101), with multi-class Support Vector Machine (SVM) to classify and transfer learning from the pre-trained CNN models to extract and classify features. Thus, the proposed framework takes an X-ray image size of 224 x 224 pixels as an input and gives the final prediction Normal or Pneumonia. The experimental results showed that the classification models proved to be effective, with an accuracy range of 97% to 98.3%. More precisely, the image extraction features using Resnet50 + SVM and Transfer Learning + Resnet50 methods achieve the highest performance of Accuracy of 98.3% and 97.8%, respectively. VL - 5 IS - 2 ER -
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