High-throughput neuron fluorescence imaging through artificial intelligence

• Main Author: Qiu, Ruidi
• Other Authors: Y. C. Chen
• Format: Thesis-Master by Coursework
• Language: English
• Published: Nanyang Technological University 2021
• Subjects: Engineering::Electrical and electronic engineering::Electronic systems::Biometrics; Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
• Online Access: https://hdl.handle.net/10356/151400
• Institution: Nanyang Technological University

Fluorescence image analysis is a commonly used method in biological image processing. In practice, different dyes correspond to different staining structures inside the cell. It is difficult for us to manually analyze and correlate images, as it is a tedious process and image interpretation is subjective from person to person. Deep learning has proven to be successful in image classification field. In recent years, it has been widely used in the field of biological image analysis. This project will start from the processing of fluorescence image to tuning parameter of designed convolution neural network. The goal of this project is to build a deep learning model to classify different types of neuron cells for biomedical detection.