Show/Hide Menu
Hide/Show Apps
anonymousUser
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Açık Bilim Politikası
Açık Bilim Politikası
Frequently Asked Questions
Frequently Asked Questions
Browse
Browse
By Issue Date
By Issue Date
Authors
Authors
Titles
Titles
Subjects
Subjects
Communities & Collections
Communities & Collections
A Multi-camera system for automation of mouse grimace scaling using convolutional neural networks
Download
index.pdf
Date
2019
Author
Ağca, Ahmet
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
3
views
0
downloads
Over the past decade, convolutional neural networks (CNNs) have gained great progress on the area of computer vision. Many problems related to automation of image recognition or classification are now possible to be solved using CNN with an accuracy much more than a human can achieve. One of these problems is the automation of Mouse Grimace Scaling (MGS). It is such a time consuming and error-prone task even for an expert to classify the pain levels of a mouse for lots of images captured from videos. For this reason, it is essential to incorporate the benefits of popular machine learning algorithms into this research area. The purpose of this thesis is to achieve significant results for practical implementations along with improving the methodology for automation of MGS. In this thesis, a complete set of methodology starting from the mouse monitoring setup to building the neural network model for automation of MGS was studied and the results were compared with that of previous works. For detecting the mouse in video frames, the previously developed tracker algorithms and detection networks were used without change. The evaluation was performed by means of both classification and regression problem and transfer learning was adopted for the basis of the study. For regression, MAE of 0.226 was achieved for one cross-validation (CV) balanced set, and 0.26 was achieved for overall balanced sets (score range, 0 to 2). For binary-classification, 91.10% accuracy was achieved for one CV balanced set, while 82.45% was achieved for overall balanced sets
Subject Keywords
Computer vision.
,
Keywords: Mouse Grimace Scaling
,
Convolutional Neural Network
,
Transfer Learning
,
Regression
,
Automation.
URI
http://etd.lib.metu.edu.tr/upload/12624607/index.pdf
https://hdl.handle.net/11511/44534
Collections
Graduate School of Natural and Applied Sciences, Thesis