Research and Application of Deep Learning in Medical Image Reconstruction and Enhancement
DOI:
https://doi.org/10.54097/8w12d064Keywords:
Medical Image, Deep Learning, Reconstruction, EnhancementAbstract
In recent years, deep learning technology has made remarkable progress in medical image reconstruction and enhancement, and has become one of the research hotspots in the field of medical image processing. This paper discusses the latest research progress and application of deep learning in medical image reconstruction and enhancement. Firstly, the importance of medical image reconstruction and enhancement and the limitations of traditional methods are introduced. Then, a detailed discussion was conducted on the application of deep learning models, including Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), and Autoencoders, in medical image processing. Specifically, an analysis and comparison were conducted on the image reconstruction ability of CNN models, the image enhancement effect of GAN models, and the image denoising and reconstruction of Autoencoder models. Then, the advantages and challenges of deep learning model in medical image processing are discussed, and the future development direction is discussed. Finally, the research results of this paper are summarized and the prospect of future research is put forward. The research in this paper provides some enlightenment and reference for researchers and practitioners in the field of medical image processing, which is helpful to promote the continuous innovation and progress of medical image processing technology.
Downloads
References
Sun, T. , Wang, X. , Lin, D. , Bao, R. , Jiang, D. , & Ding, B. , et al. (2021). Medical image security authentication method based on wavelet reconstruction and fractal dimension:. International Journal of Distributed Sensor Networks, 17(4), 1-33.
Wang, Y. , Ge, X. , Ma, H. , Qi, S. , & Yao, Y. (2021). Deep learning in medical ultrasound image analysis: a review. IEEE Access, 2021(99), 1-1.
Wang, G. (2019). High temporal-resolution dynamic pet image reconstruction using a new spatiotemporal kernel method. IEEE Transactions on Medical Imaging, 38(3), 664-674.
Weimin WANG, Yufeng LI, Xu YAN, Mingxuan XIAO, & Min GAO. (2024). Enhancing Liver Segmentation: A Deep Learning Approach with EAS Feature Extraction and Multi-Scale Fusion. International Journal of Innovative Research in Computer Science & Technology, 12(1), 26–34. Retrieved from https://ijircst. irpublications. org/index.php/ ijircst/ article/view/21.
Hunt, D. , Dighe, M. , Gatenby, C. , & Studholme, C. (2019). Automatic, age consistent reconstruction of the corpus callosum guided by coherency from in utero diffusion-weighted mri. IEEE Transactions on Medical Imaging, 2019 (99), 1-1.
Chen, R. , Huang, J. , Li, B. , Wang, J. , & Wang, H. (2020). Technologies for magnetic induction tomography sensors and image reconstruction in medical assisted diagnosis: a review. Review of Scientific Instruments, 2020(9), 91.
Liao, H. , Lin, W. , Zhou, S. , & Luo, J. (2020). Adn: artifact disentanglement network for unsupervised metal artifact reduction. IEEE transactions on medical imaging, 39(3), 634-643.
Ravi, M. , Sewa, A. , Shashidhar, T. G. , & Sanagapati, S. S. S. (2019). Fpga as a hardware accelerator for computation intensive maximum likelihood expectation maximization medical image reconstruction algorithm. IEEE Access, 2019(99), 1-1.
More, S. , Singla, J. , Verma, S. , Kavita, & Ra, I. H. (2020). Security assured cnn-based model for reconstruction of medical images on the internet of healthcare things. IEEE Access, 2020(99), 1-1.
Zhao, C. , Martin, T. , Shao, X. , Alger, J. R. , & Wang, D. J. (2020). Low dose ct perfusion with k-space weighted image average (kwia). IEEE Transactions on Medical Imaging, 2020(99), 1-1.
Xu, Y. , Zhang, N. , Li, L. , Sang, G. , & Wei, P. (2021). Joint learning of super-resolution and perceptual image enhancement for single image. IEEE Access, 2021(99), 1-1.
Galvez, A. , Iglesias, A. , Fister, I. , Otero, C. , & Diaz, J. A. (2021). Nurbs functional network approach for automatic image segmentation of macroscopic medical images in melanoma detection. Journal of computational science, 2021(9), 56.
Wang, G. , Jacob, M. , Mou, X. , Shi, Y. , & Eldar, Y. C. (2021). Deep tomographic image reconstruction: yesterday, today, and tomorrow--editorial for the 2nd special issue "machine learning for image reconstruction". IEEE Transactions on Medical Imaging, 2021(11), 40.
Ye, S. , Ravishankar, S. , Long, Y. , & Fessler, J. (2020). Spultra: low-dose ct image reconstruction with joint statistical and learned image models. IEEE transactions on medical imaging, 39(3), 729-741.
Ma, D., Li, S., Dang, B., Zang, H., & Dong, X. (2024). Fostc3net: A Lightweight YOLOv5 Based On the Network Structure Optimization. arXiv preprint arXiv:2403.13703.
Zhang, X. , Guo, D. , Huang, Y. , Chen, Y. , & Qu, X. (2020). Image reconstruction with low-rankness and self-consistency of k-space data in parallel mri. Medical Image Analysis, 63(6), 101687.
Dai, W., Tao, J., Yan, X., Feng, Z., & Chen, J. (2023, November). Addressing Unintended Bias in Toxicity Detection: An LSTM and Attention-Based Approach. In 2023 5th International Conference on Artificial Intelligence and Computer Applications (ICAICA) (pp. 375-379). IEEE.
Xiao, Y., Wang, T., Sun, X., Li, Y., Song, Y., Cui, J., ... & Gehringer, E. F. (2022, January). Modeling review helpfulness with augmented transformer neural networks. In 2022 IEEE 16th International Conference on Semantic Computing (ICSC) (pp. 83-90). IEEE.
Xin, Y., Du, J., Wang, Q., Yan, K., & Ding, S. (2023). MmAP: Multi-modal Alignment Prompt for Cross-domain Multi-task Learning. arXiv preprint arXiv:2312.08636.
Ma, D., Dang, B., Li, S., Zang, H., & Dong, X. (2023). Implementation of computer vision technology based on artificial intelligence for medical image analysis. International Journal of Computer Science and Information Technology, 1 (1), 69-76.
Liu, C., Cui, J., Shang, R., Xiao, Y., Jia, Q., & Gehringer, E. (2022). Improving Problem Detection in Peer Assessment through Pseudo-Labeling Using Semi-Supervised Learning. International Educational Data Mining Society.
Yufeng Li, Weimin Wang, Xu Yan, Min Gao, & MingXuan Xiao. (2024). Research on the Application of Semantic Network in Disease Diagnosis Prompts Based on Medical Corpus. International Journal of Innovative Research in Computer Science & Technology, 12(2), 1–9. Retrieved from https:// ijircst. irpublications. org/index. php/ijircst/ article/ view/ 29.
Xu, H., Wen, S., Gimenez, A., Gamblin, T., & Liu, X. (2017, May). DR-BW: identifying bandwidth contention in NUMA architectures with supervised learning. In 2017 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 367-376). IEEE.
Xin, Y., Du, J., Wang, Q., Lin, Z., & Yan, K. (2023). VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding. arXiv preprint arXiv:2312.08733.
Hao Xu, Shuang Song, and Ze Mao. 2023. Characterizing the Performance of Emerging Deep Learning, Graph, and High Performance Computing Workloads Under Interference. arXiv:2303.15763.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Frontiers in Computing and Intelligent Systems

This work is licensed under a Creative Commons Attribution 4.0 International License.