Refining the Quality of Chest X-Ray Images to Foster Better Classification Results via Convolutional Neural Networks

R. Ganapathi; S. Swathi Charen; Arvind M A; B.D. Kiran Kumar

doi:https://doi.org/10.47001/ICJES/2025.408004

Authors

R. Ganapathi

Dept. of Electrical & Electronics Engineering, Mar Baselios College Of Engineering and Technology, Trivandrum, Kerala, India

S. Swathi Charen

Dept. of Electrical & Electronics Engineering, Mar Baselios College Of Engineering and Technology, Trivandrum, Kerala, India

Arvind M A

Dept. of Electrical & Electronics Engineering, Mar Baselios College Of Engineering and Technology, Trivandrum, Kerala, India

B.D. Kiran Kumar

Dept. of Electrical & Electronics Engineering, Mar Baselios College Of Engineering and Technology, Trivandrum, Kerala, India

Abstract

The significance of medical imaging is crucial in diagnosing a variety of diseases, especially respiratory ailments. Nonetheless, noise artifacts such as salt-and-pepper noise and Gaussian noise can severely degrade the quality of chest X-ray (CXR) images, which may lead to incorrect diagnoses. This study seeks to enhance CXR images through the application of noise removal techniques, followed by histogram equalization to improve the overall quality of the images. Two datasets are employed: one sourced from a public database and another gathered from laboratory settings. The latter dataset undergoes a manual noise removal procedure to guarantee enhanced image clarity. Following this, a Convolutional Neural Network (CNN) model, specifically ResNet-50, is utilized for classification across both datasets. A comparative analysis is performed to illustrate that images subjected to manual denoising achieve higher accuracy compared to those that retain noise. The experimental results validate the efficacy of the proposed method in improving image quality and diagnostic precision.

Keywords

Chest X-Ray Image processing CXR Salt and Pepper Noise Gaussian Noise Respiratory system Convolutional Neural Network CNN

Citation of this Article

R. Ganapathi, S. Swathi Charen, Arvind M A, & B.D. Kiran Kumar. (2025). Refining the Quality of Chest X-Ray Images to Foster Better Classification Results via Convolutional Neural Networks. International Current Journal of Engineering and Science (ICJES), 4(8), 21-24. Article DOI: https://doi.org/10.47001/ICJES/2025.408004

References

K. Suzuki, "A review of deep learning in medical imaging: Image segmentation and disease detection," Artificial Intelligence in Medicine, 2020.
Sharif, Muhammad, Ayyaz Hussain, Muhammad Arfan Jaffar, and Tae-Sun Choi. "Fuzzy similarity based non local means filter for Rician noise removal." Multimedia tools and applications 74, no. 15 (2015): 5533-5556.
Zhang, Xinyuan, GuirongHou, Jianhua Ma, Wei Yang, Bingquan Lin, Yikai Xu, Wufan Chen, and Yanqiu Feng. "Denoising MR images using non-local means filter with combined patch and pixel similarity." PloS one 9, no. 6 (2014): e100240.
Zhou, Min-Xiong, Xu Yan, Hai-Bin Xie, Hui Zheng, Dongrong Xu, and Guang Yang. "Evaluation of nonlocal means based denoising filters for diffusion kurtosis imaging using a new phantom." PloS one 10, no. 2 (2015): e0116986.
Yang, Jian, Jingfan Fan, Danni Ai, Shoujun Zhou, Songyuan Tang, and Yongtian Wang. "Brain MR image denoising for Rician noise using pre-smooth non-local means filter." Biomedical engineering online 14, no. 1 (2015): 2.
Manjón, José V., Pierrick Coupé, and Antonio Buades. "MRI noise estimation and denoising using non-local PCA." Medical image analysis 22, no. 1 (2015): 35-47.
M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and S. M. Khan, "Medical image analysis using convolutional neural networks: A review," Journal of Medical Systems, 2018.
H. J. Kang, J. H. Lee, and Y. H. Cho, "Noise reduction methods for medical X-ray images using deep learning techniques," IEEE Transactions on Medical Imaging, 2021.
R. Wang and Y. Zhao, "Denoising of medical images using wavelet transform and CNN-based models," IEEE Access, 2020.
P. G. Howard, B. S. Deveraj, and M. I. Patel, "Enhancement of chest X-ray images using adaptive histogram equalization," IEEE Transactions on Biomedical Engineering, 2019.
T. Y. Liu, H. C. Hsu, and K. M. Lin, "A novel deep learning framework for lung disease classification using enhanced chest X-ray images," IEEE Journal of Biomedical and Health Informatics, 2021.
M. R. Alshaye, A. H. Raza, and C. J. Simmons, "Noise reduction in X-ray images using hybrid filtering techniques," IEEE Transactions on Image Processing, 2021.
N. Gupta and P. Singh, "Comparative analysis of histogram equalization techniques in medical imaging applications," IEEE Signal Processing Letters, 2021.

[1] K. Suzuki, "A review of deep learning in medical imaging: Image segmentation and disease detection," Artificial Intelligence in Medicine, 2020.

[2] Sharif, Muhammad, Ayyaz Hussain, Muhammad Arfan Jaffar, and Tae-Sun Choi. "Fuzzy similarity based non local means filter for Rician noise removal." Multimedia tools and applications 74, no. 15 (2015): 5533-5556.

[3] Zhang, Xinyuan, GuirongHou, Jianhua Ma, Wei Yang, Bingquan Lin, Yikai Xu, Wufan Chen, and Yanqiu Feng. "Denoising MR images using non-local means filter with combined patch and pixel similarity." PloS one 9, no. 6 (2014): e100240.

[4] Zhou, Min-Xiong, Xu Yan, Hai-Bin Xie, Hui Zheng, Dongrong Xu, and Guang Yang. "Evaluation of nonlocal means based denoising filters for diffusion kurtosis imaging using a new phantom." PloS one 10, no. 2 (2015): e0116986.

[5] Yang, Jian, Jingfan Fan, Danni Ai, Shoujun Zhou, Songyuan Tang, and Yongtian Wang. "Brain MR image denoising for Rician noise using pre-smooth non-local means filter." Biomedical engineering online 14, no. 1 (2015): 2.

[6] Manjón, José V., Pierrick Coupé, and Antonio Buades. "MRI noise estimation and denoising using non-local PCA." Medical image analysis 22, no. 1 (2015): 35-47.

[7] M. Anwar, M. Majid, A. Qayyum, M. Awais, M. Alnowami, and S. M. Khan, "Medical image analysis using convolutional neural networks: A review," Journal of Medical Systems, 2018.

[8] H. J. Kang, J. H. Lee, and Y. H. Cho, "Noise reduction methods for medical X-ray images using deep learning techniques," IEEE Transactions on Medical Imaging, 2021.

[9] R. Wang and Y. Zhao, "Denoising of medical images using wavelet transform and CNN-based models," IEEE Access, 2020.

[10] P. G. Howard, B. S. Deveraj, and M. I. Patel, "Enhancement of chest X-ray images using adaptive histogram equalization," IEEE Transactions on Biomedical Engineering, 2019.

[11] T. Y. Liu, H. C. Hsu, and K. M. Lin, "A novel deep learning framework for lung disease classification using enhanced chest X-ray images," IEEE Journal of Biomedical and Health Informatics, 2021.

[12] M. R. Alshaye, A. H. Raza, and C. J. Simmons, "Noise reduction in X-ray images using hybrid filtering techniques," IEEE Transactions on Image Processing, 2021.

[13] N. Gupta and P. Singh, "Comparative analysis of histogram equalization techniques in medical imaging applications," IEEE Signal Processing Letters, 2021.