Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149926
Title: Machine learning for microscopy image analysis
Authors: Azry, Amerul Tajuddin
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Azry, A. T. (2021). Machine learning for microscopy image analysis. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/149926
Abstract: This report is on the Final Year Project “Machine Learning for Microscopy Image Analysis”. The report aims to document the research, learning, results obtained, issues experienced, and possible future work required to refine the results. The report will include the motivation of the project, its objectives and an overview of how and what methods are used as well as the frameworks that were utilised for the project. A literature review contains the research on the topic as well as the various tools and programming languages used. It will include the process of how a neural network is created and the reasoning behind the choices to include in the project. The progress of the project is also documented and contain the possible reasonings why certain choices made in the design did not perform as well as other designs. The report will end with a conclusion and work that could done to further improve the design of the network. This research project is based on microscopy image analysis and how machine learning can be used for work such as detection and classification to aid in the speed of processing multiple images where traditional methods fall off.
URI: https://hdl.handle.net/10356/149926
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Final_Report_Amerul_U1821556K.pdf
  Restricted Access
3.36 MBAdobe PDFView/Open

Page view(s)

75
Updated on Jan 23, 2022

Download(s)

3
Updated on Jan 23, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.