Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures
Book chapter
Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. 2022. Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures. in: Wani, M. and Palade, V. (ed.) Deep Learning Applications, Volume 4 Springer.
Chapter title | Identification of human papillomavirus from super resolution microscopic images generated using deep learning architectures |
---|---|
Authors | Gao, X., Wen, S., Li, D., Liu, W., Xiong, J., Xu, B., Liu, J., Zhang, H. and Liu, X. |
Abstract | This chapter presents five deep learning architectures for identification of Human papillomavirus (HPV) through generation of super resolution (SR) images by 4 folds. Specifically, generative adversarial deep learning networks (GAN) and a texture-based vision transformer (TTSR) architec-ture are applied and evaluated. As such, the generated SR images are able to display the same way a high-resolution image offers in identification of HPV like structures. In comparison, TTSR appears to perform the best with PSNR and SSIM being 28.70 and 0.8778 respectively whereas 25.80/0.7910, 18.35/0.5059. 30.31/0.8013, and 28.07/0.6074 are observed for the methods of RCAN, Pix2Pix, CycleGAN, and ESRGAN respective-ly. With regard to sensitivity and specificity when detecting HPV clus-ters, TTSR also leads with 83.6% and 83.33% respectively. It appears the computational SR images are capable to differentiate distinguishing fea-tures of HPV like particles and to determine the effectiveness of anti-HPV agents, holding promise providing insights into the formation stage of a cancer from HPV in the near future. |
Sustainable Development Goals | 4 Quality education |
Book title | Deep Learning Applications, Volume 4 |
Editors | Wani, M. and Palade, V. |
Publisher | Springer |
Series | Advances in Intelligent Systems and Computing (AISC) |
ISBN | |
Hardcover | 9789811961526 |
Electronic | 9789811961533 |
Publication dates | |
26 Nov 2022 | |
Online | 25 Nov 2022 |
Publication process dates | |
Deposited | 07 Jun 2022 |
Accepted | 17 Mar 2022 |
Output status | Published |
Accepted author manuscript | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-981-19-6153-3_1 |
Language | English |
https://repository.mdx.ac.uk/item/89wx4
Download files
83
total views1
total downloads2
views this month1
downloads this month