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
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