1,187
Views
0
CrossRef citations to date
0
Altmetric
Research Article

GACSNet: A Lightweight Network for the Noninvasive Blood Glucose Detection

ORCID Icon, ORCID Icon, ORCID Icon &
Article: 2081898 | Received 10 Dec 2021, Accepted 17 May 2022, Published online: 05 Jun 2022

References

  • Cho, O. K., Y. O. Kim, H. Mitsumaki, and K. Kuwa. 2004. Noninvasive measurement of glucose by metabolic heat conformation method. Clinical Chemistry 50 (10):1894–1116. doi:10.1373/clinchem.2004.036954.
  • Delbeck, S., T. Vahlsing, S. Leonhardt, G. Steiner, and H. M. Heise. 2019. Non-invasive monitoring of blood glucose using optical methods for skin spectroscopy—opportunities and recent advances. Analytical and Bioanalytical Chemistry 411 (1):63–77. doi:10.1007/s00216-018-1395-x.
  • Frid-Adar, M., I. Diamant, E. Klang, M. Amitai, J. Goldberger, and H. Greenspan. 2018. GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification. Neurocomputing 321:321–31. doi:10.1016/j.neucom.2018.09.013.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA, 770–78. IEEE.
  • He, M., Y. Wu, and R. Yang. 2019. Research on nondestructive blood glucose cloud detection system based on improved deep regression network. Journal of System Simulation 31 (11):2492. doi:10.16182/j.1004731x.joss.19-FZ0357.
  • Inoue, H. 2018. Data Augmentation by Pairing Samples for Images Classification. arXiv preprint arXiv:1801.02929. http://arxiv.org/abs/1801.02929.
  • Li, D., and S. Jia. 2017. Application of BP artificial neural network in blood glucose prediction based on multi-spectrum. Laser & Optoelectronics Progress 54 (3):031703. doi:10.3788/lop54.031703.
  • Li, Y., X. Li, C. Xiao, H. Li, and W. Zhang. 2021. EACNet: enhanced asymmetric convolution for real-time semantic segmentation. IEEE Signal Processing Letters 28:234–38. doi:10.1109/LSP.2021.3051845.
  • Nagendran, M., Y. Chen, C. A. Lovejoy, A. C. Gordon, M. Komorowski, H. Harvey, E. J. Topol, J. P. A. Ioannidis, G. S. Collins, and S. Maruthappu. 2020. Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies. BMJ368. doi:10.1136/bmj.m689.
  • Nasiri, A., A. Taheri-Garavand, M. Omid, and G. M. Carlomagno. 2019. Intelligent fault diagnosis of cooling radiator based on deep learning analysis of infrared thermal images. Applied Thermal Engineering 163:114410. doi:10.1016/j.applthermaleng.2019.114410.
  • Ornek, A. H., M. Ceylan, and S. Ervural. 2019. Health status detection of neonates using infrared thermography and deep convolutional neural networks. Infrared Physics & Technology 103 (103044):103044. doi:10.1016/j.infrared.2019.103044.
  • Ring, E. F. J., and K. Ammer. 2012. Infrared thermal imaging in medicine. Physiological Measurement 33 (3):R33. doi:10.1088/0967-3334/33/3/r33.
  • Roncon, L., M. Zuin, G. Rigatelli, and G. Zuliani. 2020. Diabetic patients with COVID-19 infection are at higher risk of ICU admission and poor short-term outcome. Journal of Clinical Virology 127:104354. doi:10.1016/j.jcv.2020.104354.
  • Sandler, M., A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen. 2018. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Salt Lake City, UT, USA, 4510–20. IEEE.
  • Schlemper, J., O. Oktay, M. Schaap, M. Heinrich, B. Kainz, B. Glocker, and D. Rueckert. 2019. Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Analysis 53 (53):197–207. doi:10.1016/j.media.2019.01.012.
  • Seidman, D. S., I. Ashkenazi, R. Arnon, Y. Shapiro, and Y. Epstein. 1991. The effects of glucose polymer beverage ingestion during prolonged outdoor exercise in the heat. Medicine and Science in Sports and Exercise 23 (4):458–62. doi:10.1249/00005768-199104000-00011.
  • Selvarani, A., and G. R. Suresh. 2019. Infrared thermal imaging for diabetes detection and measurement. Journal of Medical Systems. Journal of Medical Systems 43 (2):1–11. doi:10.1007/s10916-018-1140-1.
  • Sengupta, S., A. Handoo, I. Haq, K. Dahiya, S. Mehta, and M. Kaushik. 2022. Clarke error grid analysis for performance evaluation of glucometers in a tertiary care referral hospital. Indian Journal of Clinical Biochemistry 37 (2):199–205. doi:10.1007/s12291-021-00971-4.
  • Sim, J. Y., C.-G. Ahn, E.-J. Jeong, and B. K. Kim. 2018. In vivo microscopic photoacoustic spectroscopy for non-invasive glucose monitoring invulnerable to skin secretion products. Scientific Reports 8 (1):1–11. doi:10.1038/s41598-018-19340-y.
  • Speight, J., E. Holmes‐Truscott, C. Hendrieckx, S. Skovlund, and D. Cooke. 2020. Assessing the impact of diabetes on quality of life: What have the past 25 years taught us? Diabetic Medicine 37 (3):483–92. doi:10.1111/dme.14196.
  • Sun, T., H. Li, and W. Song. 2018. Sub-health infrared thermal imaging evaluation. Beijing: China Traditional Chinese Medicine Publishing House.
  • Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA, 2818–26. IEEE.
  • Tang, L., S. J. Chang, C. J. Chen, and J. T. Liu. 2020. Non-invasive blood glucose monitoring technology: A review. Sensors 20 (23):6925. doi:10.3390/S20236925.
  • Wahab, N., A. Khan, and Y. S. Lee. 2019. Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images. Microscopy 68 (3):216–33. doi:10.1093/jmicro/dfz002.
  • Wu, X., P. Wu, M. Gu, and J. Xue. 2020. Ratiometric fluorescent probe based on AuNCs induced AIE for quantification and visual sensing of glucose. Analytica Chimica Acta 1104:140–46. doi:10.1016/j.aca.2020.01.004.
  • Yamashita, R., M. Nishio, R. K. G. Do, and K. Togashi. 2018. Convolutional neural networks: An overview and application in radiology. Insights into Imaging 9 (4):611–29. doi:10.1007/s13244-018-0639-9.
  • Yao, G., T. Lei, and J. Zhong. 2019. A review of convolutional-neural-network-based action recognition. Pattern Recognition Letters 118:14–22. doi:10.1016/j.patrec.2018.05.018.
  • Zhai, Q., S. Gong, Y. Wang, Q. Lyu, Y. Liu, Y. Ling, J. Wang, G. P. Simon, and W. Cheng. 2019. Enokitake mushroom-like standing gold nanowires toward wearable noninvasive bimodal glucose and strain sensing. ACS Applied Materials & Interfaces 11 (10):9724–29. doi:10.1021/acsami.8b19383.
  • Zhang, X., X. Zhou, M. Lin, and J. Sun. 2018. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Salt Lake City, UT, USA, 6848–56. IEEE.
  • Zhao, Z., and R. Myllylä. 2005. Measuring the optical parameters of weakly absorbing, highly turbid suspensions by a new technique: Photoacoustic detection of scattered light. Applied Optics 44 (36):7845–52. doi:10.1364/AO.44.007845.
  • Zhou, Y., S. Chen, Y. Wang, and W. Huan. 2020. Review of research on lightweight convolutional neural networks. In 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) Seattle, WA, USA, 1713–20. IEEE.