Deep learning approach for laboratory mice grimace scaling

Date

2016

Author

Eral, Mustafa

Metadata

Show full item record

Item Usage Stats

242
views

0
downloads

Deep learning is extremely attractive research topic in pattern recognition and machine learning areas. Applications in speech recognition, natural language processing, and machine vision fields gained huge acceleration in performance by employing deep learning. In this thesis, deep learning is used for medical purposes in order to scale pain degree of drug stimulated mice by examining facial grimace. For this purpose each frame in the videos in the training set were scaled manually by experts according to Mouse Grimace Scaling(MGS) manual and these frames were used for training a convolutional neural network. For testing the network, another set of videos which was not used for training before, was used. In order to show the classification power of convolutional neural networks, the same classification tasks are performed with some classic kernel based machine learning algorithms and results are compared. For training and testing, a workstation having two powerful graphic card(GPU) is used.

Subject Keywords

Neural networks (Computer science)., Visual texture recognition., Artificial intelligence., Machine learning., Face perception.

URI

http://etd.lib.metu.edu.tr/upload/12620517/index.pdf
https://hdl.handle.net/11511/25948

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

Learning to play an imperfect information card game using reinforcement learning Alpaslan, Ferda Nur; Baykal, Ömer; Demirdöver, Buğra Kaan (2022-08-01) Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges d...
Deep Learning-Based Hybrid Approach for Phase Retrieval IŞIL, ÇAĞATAY; Öktem, Sevinç Figen; KOÇ, AYKUT (2019-06-24) We develop a phase retrieval algorithm that utilizes the hybrid-input-output (HIO) algorithm with a deep neural network (DNN). The DNN architecture, which is trained to remove the artifacts of HIO, is used iteratively with HIO to improve the reconstructions. The results demonstrate the effectiveness of the approach with little additional cost.
Time series classification using deep learningTime series classification using deep learning Hatipoğlu, Poyraz Umut; İyigün, Cem; Department of Industrial Engineering (2016) Deep learning is a fast-growing and interesting field due to the need to represent statistical data in a more complex and abstract way. Development in the processors and graphics processing unit technology effects undeniably that the deep networks get that popularity. The main purpose of this work is to develop robust and full functional time series classification method. To achieve this intent a deep learning based novel methods are proposed. Because time series data can have complex and variable structure...
Computational representation of protein sequences for homology detection and classification Oğul, Hasan; Mumcuoğlu, Ünal Erkan; Department of Information Systems (2006) Machine learning techniques have been widely used for classification problems in computational biology. They require that the input must be a collection of fixedlength feature vectors. Since proteins are of varying lengths, there is a need for a means of representing protein sequences by a fixed-number of features. This thesis introduces three novel methods for this purpose: n-peptide compositions with reduced alphabets, pairwise similarity scores by maximal unique matches, and pairwise similarity scores by...
Deep Learning-Enabled Technologies for Bioimage Analysis Rabbi, Fazle; Dabbagh, Sajjad Rahmani; Angın, Pelin; Yetisen, Ali Kemal; Tasoglu, Savas (2022-02-01) Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of em...

Citation Formats

M. Eral, “Deep learning approach for laboratory mice grimace scaling,” M.S. - Master of Science, Middle East Technical University, 2016.