Machine learning for cellular laser images and spectral data

• Main Author: Wu, Chenzhou
• Other Authors: -
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2020
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/139193
• Institution: Nanyang Technological University

There has been much heard that biolasers has full applications in medicine, communications, imaging, industry, electronics, and military. Tissue-biolasers are significant in monitoring or detecting subtle biological transients in tissue. Aldo improved signal to background ratio(contrast) and sensitivity. Moreover, it mimics real complex natural environments in the body and highly sensitive on-chip biosensing or biomedical imaging. Bioaser has been mostly used like a switch on/off signal by utilizing laser spectra for biosensing. By mapping laser emissions from biological samples to images is the first breakthrough. There is full information of laser modes, for example: the intelligence behind every laser pattern. The objective of this project is to use machine learning algorithms to analyze and classify images and spectral data from biological lasers to model the evolution of cancer cells for biological prediction tasks. Classify the process of the images has been approached by LabView, Matlab, and Python language. In this project, there are different software to achieve on objective. The result from classification and analysis data should lead to a clear picture of the relationship between no. of laser modes and the size of laser that helps lab users to go for the next experiment. Some research and implantation are studied to compare the advantages and disadvantages of different software. Results have demonstrated the output and analysis.