Diagnosis of dengue virus infection using spectroscopic images and deep learning

PeerJ Computer Science

Muhammad Saleem¹  Hani Alquhayz²  Mehdi Hassan³  Syed Fahad Tahir³  Muhammad Sanaullah⁴  Labiba Gillani Fahad⁵  Jin Young Kim⁶  Safdar Ali⁷ 

[1] Agriculture & Biophotonics Division, National Institute of Lasers and Optronics College, Pakistan Institute of Engineering and Applied Sciences (NILOP-C, PIEAS), Lehtrar Road, Nilore, Islamabad, Pakistan;Department of Computer Science and Information, College of Science in Zulfi, Majmaah University,Al-Majmaah, Saudi Arabia;Department of Computer Science, Air University, Islamabad, Pakistan;Department of Computer Science, Bahaudian Zakaria University, Multan, Pakistan;Department of Computer Science, National University of Computing and Emerging Sciences, FAST-NUCES, Islamabad, Pakistan;Department of ICT Convergence System Engineering, Chonnam National University, Gwangju, South Korea;Directorate of National Repository, Islamabad, Pakistan;

关键词: Dengue;    Deep Learning;    Raman Spectroscopy;    Plasma;    Spectra;   

DOI  :  10.7717/peerj-cs.985

来源: DOAJ

【 摘 要 】

Dengue virus (DENV) infection is one of the major health issues and a substantial epidemic infectious human disease. More than two billion humans are living in dengue susceptible regions with annual infection mortality rate is about 5%–20%. At initial stages, it is difficult to differentiate dengue virus symptoms with other similar diseases. The main objective of this research is to diagnose dengue virus infection in human blood sera for better treatment and rehabilitation process. A novel and robust approach is proposed based on Raman spectroscopy and deep learning. In this regard, the ResNet101 deep learning model is modified by exploiting transfer learning (TL) concept on Raman spectroscopic data of human blood sera. Sample size was selected using standard statistical tests. The proposed model is evaluated on 2,000 Raman spectra images in which 1,200 are DENV-infected of human blood sera samples, and 800 are healthy ones. It offers 96.0% accuracy on testing data for DENV infection diagnosis. Moreover, the developed approach demonstrated minimum improvement of 6.0% and 7.0% in terms of AUC and Kappa index respectively over the other state-of-the-art techniques. The developed model offers superior performance to capture minute Raman spectral variations due to the better residual learning capability and generalization ability compared to others deep learning models. The developed model revealed that it might be applied for diagnosis of DENV infection to save precious human lives.

【 授权许可】

Unknown