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

226
views

146
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

Suggestions

OpenMETU
Core

A new approach to mathematical water quality modeling in reservoirs: Neural networks Karul, C; Soyupak, S; Germen, E (1998-01-01) Neural Networks are becoming more and more valuable tools for system modeling and function approximation as computing power of microcomputers increase. Modeling of complex ecological systems such as reservoir limnology is very difficult since the ecological interactions within a reservoir are difficult to define mathematically and are usually system specific. To illustrate the potential use of Neural Networks in ecological modeling, a software was developed to train the data from Keban Dam Reservoir by back...
Deep convolutional neural networks for airport detection in remote sensing images Budak, Umit; Sengur, Abdulkadir; Halıcı, Uğur (2018-05-05) This study investigated the use of deep convolutional neural networks (CNNs) in providing a solution for the problem of airport detection in remote sensing images (RSIs). In recent years, Deep CNNs have gained much attention with numerous applications having been undertaken in the area of computer vision. Researchers generally approach airport detection as a pattern recognition problem, in which first various distinctive features are extracted, and then a classifier is adopted to detect airports. CNNs not o...
A new neural network approach to the target tracking problem with smart structure Caylar, Selcuk; Leblebicioğlu, Mehmet Kemal; Dural, Gülbin (2006-12-01) The algorithm presented in this paper, namely the modified neural multiple source tracking algorithm (MN-MUST) is the modified form of the recently published work, a NN algorithm, the neural multiple-source tracking (N-MUST) algorithm, was presented for locating and tracking angles of arrival from multiple sources. MN-MUST algorithm consists of three stages that are classified as the detection, filtering and DoA estimation stages. In the first stage a number of radial basis function neural networks (RBFNN) ...
A digital neuron realization for the random neural network model CERKEZ, CUNEYT; AYBAY, HADİ IŞIK; Halıcı, Uğur (1997-06-12) In this study the neuron of the random neural network (RNN) model (Gelenbe 1989) is designed using digital circuitry. In the RNN model, each neuron accumulates arriving pulses and can fire if its potential at a given instant of time is strictly positive. Firing occurs at random, the intervals between successive firing instants following an exponential distribution of constant rate. When a neuron fires, it routes the generated pulses to the appropriate output lines in accordance with the connection probabili...
A temporal neural network model for constructing connectionist expert system knowledge bases Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01) This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.

Citation Formats

A. Ağca, “A Multi-camera system for automation of mouse grimace scaling using convolutional neural networks,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Electrical and Electronics Engineering., Middle East Technical University, 2019.