지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.5
- 수록면
- 762 - 772 (11page)
- DOI
- 10.7840/kics.2024.49.5.762
이용수
초록· 키워드
오류제보하기
Medical imaging plays a crucial role in medical diagnosis, with chest X-ray imaging being a widely employed method for screening and diagnosing pulmonary diseases due to its cost-effectiveness. However, low-resolution images generated by expensive equipment and suboptimal imaging techniques often lead to a loss of critical features and acceptable texture. The acquisition of high-quality medical images is paramount for accurate disease diagnosis. This study introduces an innovative approach for reconstructing super-resolution medical images using deep learning techniques, explicitly targeting chest X-ray images. The proposed method, the Pathologically Invariant Remaining Enhanced Channel Attention Block (RECAB), incorporates the Exponential Linear Unit (ELU) activation function. The primary objective is to accurately recover high-resolution (HR) chest X-ray images from their low-resolution (LR) counterparts, leveraging a channel attention mechanism and convolution layer with the ELU activation function―the evaluation of the proposed method involved two datasets, X-Ray 2017 and X-Ray 2014. A comprehensive comparison was conducted with several state-of-the-art techniques, including GAN-based super-resolution, deep learning-based super-resolution, and interpolation-based super-resolution. The quality of the preprocessed images was assessed using the Structural Similarity Index (SSIM) and the Multi-Scale Structural Similarity Index (MSIM). The results prove the higher performance of the proposed method, which outperformed the average of 8.6% and 11.6% in SSIM and MSIM values by 11% and 12.14% on the two datasets, respectively. This research signifies a significant advancement in enhancing the resolution and quality of chest X-ray images, holding substantial potential for improving diagnostic accuracy and aiding in medical decision-making.
목차
ABSTRACT
Ⅰ. Introduction
Ⅱ. Related work
Ⅲ. Method
Ⅳ. Experiments
Ⅴ. Results and Ablation Study
Ⅵ. Conclusion
References
참고문헌 (22)
참고문헌 신청
A. A. Reshi, et al., “An efficient CNN model for COVID-19 disease detection based on X-ray image classification,” Complexity 2021, pp. 1-12, 2021. (https://doi.org/10.1155/2021/6621607)
Zhao, C. Y., Jia, R. S., Liu, Q. M., Liu, X. Y., Sun, H. M., & Zhang, X. L. (2021). Chest X-ray images super-resolution reconstruction via recursive neural network. Multimedia Tools and Applications, 80, 263-277. (https://doi.org/10.1007/s11042-020-09773-x)
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proc. ECCV, pp. 286-301, 2018. (https://doi.org/10.48550/arXiv.1807.02758)
A. Salem, H. Ibrahim, and H.-S. Kang, “Light field reconstruction using residual networks on raw images,” Sensors, vol. 22, no. 5, p. 1956, 2022. (https://doi.org/10.3390/s22051956)
B. Lim, S. Son, H. Kim, S. Nah, and M. K. Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. IEEE CVPR Wkshps., pp. 136-144, 2017. (https://doi.org/10.48550/arXiv.1707.02921)
Zhao, C. Y., Jia, R. S., Liu, Q. M., Liu, X. Y., Sun, H. M., & Zhang, X. L. (2021). Chest X-ray images super-resolution reconstruction via recursive neural network. Multimedia Tools and Applications, 80, 263-277. (https://doi.org/10.1007/s11042-020-09773-x)
Y. Zhang, K. Li, K. Li, L. Wang, B. Zhong, and Y. Fu, “Image super-resolution using very deep residual channel attention networks,” in Proc. ECCV, pp. 286-301, 2018. (https://doi.org/10.48550/arXiv.1807.02758)
A. Salem, H. Ibrahim, and H.-S. Kang, “Light field reconstruction using residual networks on raw images,” Sensors, vol. 22, no. 5, p. 1956, 2022. (https://doi.org/10.3390/s22051956)
B. Lim, S. Son, H. Kim, S. Nah, and M. K. Lee, “Enhanced deep residual networks for single image super-resolution,” in Proc. IEEE CVPR Wkshps., pp. 136-144, 2017. (https://doi.org/10.48550/arXiv.1707.02921)
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