INVESTIGATING THE EFFECTIVENESS OF ADVANCED DEEP LEARNING MODELS IN PULMONARY DISEASE CLASSIFICATION  USING CHEST X-RAY IMAGES

Nguyễn Hữu Khánh

doi:10.34238/tnu-jst.8408

INVESTIGATING THE EFFECTIVENESS OF ADVANCED DEEP LEARNING MODELS IN PULMONARY DISEASE CLASSIFICATION USING CHEST X-RAY IMAGES

About this article

Received: 25/07/23 Revised: 30/08/23 Published: 31/08/23

Authors

Nguyen Huu Khanh , Thai Nguyen University

Abstract

According to the WHO's Global Tuberculosis Report 2022, an estimated 10.6 million people are infected with Tuberculosis and 1.6 million people die from Tuberculosis worldwide. Meanwhile, pneumonia is estimated to cause more than 700,000 deaths each year globally according to the UNICEF. In Vietnam, these two diseases are still serious health problems with a large number of cases and deaths. To support effective lung disease diagnosis, the study applied deep learning techniques, especially typical CNN models such as Resnet, DenseNet, Xception, MobileNet and InceptionV3 to classify chest X-ray images. These models are applied fine-tune transfer learning and then trained and compared to find the model with the highest accuracy. The results show that the MobileNet model is the best, with an accuracy of up to 98.31%, the highest compared to other models. The research results can be deployed and developed into a medical support system that helps doctors and medical staff quickly and accurately identify lung diseases from X-ray images, thereby helping to improve the diagnosis and treatment process for patients.

Keywords

Deep learning; Convolutional Neural Network; Pulmonary Disease; X-Ray Images; Transfer Learning

Full Text:

PDF (Tiếng Việt)

References

[1] T. Kasaeva, K. Floyd, A. Baddeley, S. d. Boon, A. Dean, H. M. Dias, D. Falzon, K. Floyd, I. G. Baena, N. Gebreselassie, P. Glaziou, M. Lalli, I. Law, and P. Nguhiu, Global tuberculosis report 2022, World Health Organization, 2022.

[2] UNICEF, "Childhood pneumonia: Everything you need to know," UNICEF, November 08, 2022. [Online]. Available: https://www.unicef.org/stories/childhood-pneumonia-explained. [Accessed July 15, 2023].

[3] P. Thuong, "Experts show how to detect children with pneumonia early at home (In Vietnamese)," Health and Life, November 03, 2022. [Online]. Available: https://suckhoedoisong.vn/chuyen-gia-chi-cach-phat-hien-tre-mac-viem-phoi-som-nhat-tai-nha-169221102231244035.htm. [Accessed July 15, 2023].

[4] M. Abubakar, I. Shah, W. Ali, and F. Bashir, "Classification of Pneumonia and Tuberculosis from Chest X-rays," March 25, 2021. [Online]. Available: https://arxiv.org/abs/2103.14562. [Accessed July 15, 2023].

[5] R. Mogaveera, R. Maur, Z. Qureshi, and Y. Mane, "Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets," ITM Web of Conferences, vol. 44, 2022, Art. no. 03007, doi: 10.1051/itmconf/20224403007.

[6] S. Kudum, D. Shah, I. Vaish, and P. Tirumala, "Determining the best convolutional neural network for identifying tuberculosis and pneumonia in chest x-rays," The Journal of Emerging Investigators, vol. 5, 2022. [Online]. Available: https://emerginginvestigators.org/articles/21-047. [Accessed July 15, 2023].

[7] A.Pranaya, D.V.Sowmya, L.Poojitha, P.Grace, K.Bhavya and Dr.N.V. Ganapathi Raju, "Pneumonia Detection Using Deep Learning," E3S Web of Conferences, vol. 391, 2023, doi: 10.1051/e3sconf/ 202339101067.

[8] A. Nair, A. Mohan, J. J. Fernandez, S. B. Seema, and V. G. Sreena, "Chest X-ray Image Classification for Tuberculosis using Deep Convolutional Neural Network," National Conference on Smart Systems and Technologies, vol. 8, no. 7, pp. 96-103, 2021.

[9] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778, doi: 10.48550/arXiv.1512.03385.

[10] G. Huang, Z. Liu, L. v. d. Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 4700-4708. [Online]. Available: https://arxiv.org/abs/1608.06993. [Accessed July 15, 2023].

[11] F. Chollet, "Xception: Deep Learning with Depthwise Separable Convolutions," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251-1258. [Online]. Available: https://arxiv.org/abs/1610.02357. [Accessed July 15, 2023].

[12] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," arXiv, 2017. [Online]. Available: https://arxiv.org/abs/1704.04861. [Accessed July 15, 2023].

[13] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 2818-2826. [Online]. Available: https://arxiv.org/abs/1512.00567. [Accessed July 15, 2023].

[14] T. Rahman, A. Khandakar, M. A. Kadir, K. R. Islam, K. F. Islam, Z. B. Mahbub, M. A. Ayari, and M. E. H. Chowdhury, "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization," IEEE Access, vol. 8, pp. 191586 - 191601, 2020.

[15] D. Kermany, K. Zhang, and M. Goldbaum, "Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification," Mendeley Data, 2018. [Online]. Available: https://dx.doi.org/10.17632/rscbjbr9sj.2. [Accessed July 15, 2023].

DOI: https://doi.org/10.34238/tnu-jst.8408

Refbacks

There are currently no refbacks.



Remember me