IoT-Enhanced local attention dual networks for pathological image restoration in healthcare

被引：0
作者：
Hassan, Abdelwahab Said [1 ]
Thakare, Anuradha [2 ]
Bhende, Manisha [3 ]
Prasad, K.D.V. [4 ]
Singh, Pavitar Parkash [5 ]
Byeon, Haewon [6 ,7 ]
机构：
[1] Department of Mathematics, Faculty of Science, Suez Canal University
[2] Department of Computer Engineering, Pimpri Chinchwad College of Engineering, Pune
[3] Dr. D. Y. Patil Vidyapeeth, Pune, Dr. D. Y. Patil School of Science & Technology, Tathawade, Pune
[4] Symbios'is Institute of Business Management, Symbiosis International (Deemed University), Hyderabad, Pune
[5] Department of Management, Lovely Professional University
[6] Department of AI and Software, Inje University, Gimhae
[7] Inje University Medical Big Data Research Center, Gimhae
来源：
Measurement: Sensors | 2024年 / 33卷
关键词：
Internet of Things (IoT); Non-local attention; Pathological images; Precision medicine; Sparse coding; Super-resolution restoration;
D O I：
10.1016/j.measen.2024.101211
中图分类号：
学科分类号：
摘要：
High-resolution pathological images play a pivotal role in accurate disease diagnosis and are important in precision medicine. However, obtaining real-time high-resolution images faces challenges due to hardware limitations and scanning time constraints. Conventional image super-resolution restoration algorithms struggle to provide satisfactory results for pathological images, resulting in blurred and unrealistic restorations. In response to this challenge, this research proposes a pioneering approach by integrating Internet of Things (IoT) technology with Local Attention Dual Network (IOT-LAT) for super-resolution restoration of pathological images. The enhanced IOT-LATincorporates IoT-based data acquisition and processing, IoT-Enhanced non-local attention mechanisms, Gaussian constraint, and parameter-sharing strategies in up-sampling and down-sampling branches. This integration enables real-time super-resolution restoration of pathological images with improved accuracy. The reconstructed images exhibit a structural similarity of 0.914 and a peak signal-to-noise ratio of 30.84 dB. These results validate the effectiveness of the suggested approach in precisely reconstructing high-frequency details and improving modelling efficiency via a lightweight non-local attention mechanism enhanced by the Internet of Things. This work discusses IoT and non-local attention dual networks to improve pathological image super-resolution restoration. This allows for faster and more accurate disease diagnosis and treatment in precision medicine. © 2024 The Authors
引用
收藏
相关论文
共 31 条
  • [11] Yang H., Gao J., Wu Z., Blur identification and image super-resolution restoration using an approach similar to variable projection, IEEE Signal Process. Lett., 15, pp. 289-292, (2008)
  • [12] Kim M.-G., Oh S., Kim Y., Kwon H., Bae H.-M., Robust single-probe quantitative ultrasonic imaging system with a target-aware deep neural network, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng., 68, 12, pp. 3737-3747, (2021)
  • [13] Wang L., Kim T.-K., Yoon K.-J., Joint framework for single image restoration and super-resolution with an event camera, IEEE Trans. Pattern Anal. Mach. Intell., 44, 11, pp. 7657-7673, (2022)
  • [14] Qiu D., Cheng Y., Wang X., Progressive feedback residual attention network for cardiac magnetic resonance imaging super-resolution, IEEE. J. Biomed. Health Inf., 27, 7, pp. 3478-3488, (2023)
  • [15] Benameur S., Mignotte M., Destrempes F., De Guise J.A., Three-dimensional biplanarrestoration of scoliotic rib cage using the estimation of a mixture of probabilistic prior models, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng., 52, 10, pp. 1713-1728, (2005)
  • [16] Gunturk B.K., Gevrekci M., High-resolution image restoration from multiple differently exposed images, IEEE Signal Process. Lett., 13, 4, pp. 197-200, (2006)
  • [17] Elad M., Datsenko D., Example-based regularization deployed to super-resolution restoration of a single image, Comput. J., 52, 1, pp. 15-30, (2009)
  • [18] Patti A.J., Altunbasak Y., Artifact reduction for set theoretic super resolution image restoration with edge adaptive constraints and higher-order interpolants, IEEE Trans. Image Process., 10, 1, pp. 179-186, (2001)
  • [19] Zhao Y., Yang J., Chan J.C.-W., Hyperspectral imagery super-resolution by spatial–spectral joint nonlocal similarity, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 7, 6, pp. 2671-2679, (2014)
  • [20] Albahri A.S., Et al., Based multiple heterogeneous wearable sensors: a smart real-time health monitoring structured for hospitals distributor, IEEE Access, 7, pp. 37269-37323, (2019)
1234