Machine learning in fMRI classification

QR Code

Machine learning in fMRI classification

Statistical analysis method is utilitarian in neuroimaging. For instance, SPM12, FSL and BrainVoyager are widely used for testing the hypotheses about functional magnetic resonance imaging (fMRI). However, that testing and studying of brain images mostly consist of experts work. It is not fully auto...

Full description

Bibliographic Details
Main Authors:	Mohd Suhaimi, Nur Farahana, Htike@Muhammad Yusof, Zaw Zaw
Format:	Proceeding Paper
Language:	English
Published:	International Neuroinformatics Coordinating Facilities (INCF) 2016
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/61306/ http://irep.iium.edu.my/61306/6/61306-Machine%20learning.pdf

Similar Items

Comparison of machine learning classifiers for dimensionally reduced fMRI data using random projection and principal component analysis
by: Mohd Suhaimi, Nur Farahana, et al.
Published: (2019)

Feature map size selection for fMRI classification on end-to-end deep convolutional neural networks
by: Suhaimi, Farahana, et al.
Published: (2018)

Studies on classification of FMRI data using deep learning approach
by: Mohd Suhaimi, Nur Farahana, et al.
Published: (2015)

Evaluation of Transfer Learning Pipeline for ADHD Classification via fMRI Images
by: Nur Atiqah, Kamal, et al.
Published: (2024)

Layer-fMRI acquisition and analysis
by: Marsh, Daniel C.
Published: (2023)

The relationship between MEG and fMRI
by: Hall, Emma L., et al.
Published: (2014)

Simultaneous EEG and fMRI at high fields
by: Mullinger, Karen Julia
Published: (2008)

Functional brain imaging with fMRI and MEG
by: He, Jiabao
Published: (2005)

Oral cancer prediction using gene expression profiling and machine learning
by: K. Shams, Wafaa, et al.
Published: (2017)

State of the Art Machine Learning Techniques for Time Series Forecasting: A Survey
by: Nyein Naing, Wai Yan, et al.
Published: (2015)

Hierarchical extreme learning machine based reinforcement learning for goal localization
by: AlDahoul, Nouar, et al.
Published: (2017)

EEG-fMRI: novel methods for gradient artefact correction
by: Spencer, G. S.
Published: (2015)

Data-driven fMRI data analysis based on parcellation
by: Ji, Yongnan
Published: (2001)

Studying the selectivity of neuronal subpopulations within fMRI voxels
by: Sapountzis, Panagiotis
Published: (2010)

Interpretation of BOLD events using fMRI at 7 Tesla
by: Tan, Francisca Marie
Published: (2017)

Sensory mapping using High-Resolution 7 T fMRI
by: Asghar, Michael
Published: (2019)

Spatio-temporal fMRI data in the spiking neural network
by: Saharuddin, Shaznoor Shakira, et al.
Published: (2018)

Utilizing hierarchical extreme learning machine based reinforcement learning for object sorting
by: AlDahoul, Nouar, et al.
Published: (2019)

Application of machine learning to determine the characteristics of adjacent normal tissues in liver cancer
by: Shams, Wafaa Kazaal, et al.
Published: (2017)

fMRI study of pain threshold in the presence and absence of the loved one
by: Tamam, Sofina
Published: (2016)

Classification of Immunosignature Using Random Forests for Cancer Diagnosis
by: Zarzar, Mouayad, et al.
Published: (2015)

Spurious correlations in simultaneous EEG-fMRI driven by in-scanner movement
by: Fellner, M.-C., et al.
Published: (2016)

Studying neural selectivity for motion using high-field fMRI
by: Beckett, Alexander
Published: (2013)

Investigating Post-Task Responses using MEG and 7T fMRI
by: Coleman, Sebastian C.
Published: (2024)

Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source
by: Chowdhury, Muhammad Enamul Hoque
Published: (2014)

Cancer recurrence prediction using machine learning
by: Shoon Lei, Win, et al.
Published: (2014)

Automatic vehicle classification system
by: Saeed S, Almehmadi Tarig, et al.
Published: (2015)

Interpreting intervention induced neuroplasticity with fMRI: The case for multimodal imaging strategies
by: Reid, L., et al.
Published: (2016)

Functional neuroimaging of the somatosensory system with Ultra-high-field fMRI and MEG
by: Wang, Fan
Published: (2012)

Altered appetite in Crohn’s Disease: deconstructing the enteroendocrine axis using fMRI
by: Thapaliya, Gita
Published: (2019)

Parcellation of fMRI datasets with ICA and PLS: a data driven approach
by: Ji, Yongnan, et al.
Published: (2009)

Premalignant pancreatic cancer diagnosis using proteomic pattern analysis
by: Htike@Muhammad Yusof, Zaw Zaw
Published: (2015)

End of Project: RIGS16-350-0514
by: Htike@Muhammad Yusof, Zaw Zaw
Published: (2017)

Leukemia detection from blood smears
by: Htike@Muhammad Yusof, Zaw Zaw
Published: (2014)

Premalignant pancreatic cancer diagnosis using proteomic pattern analysis
by: Htike@Muhammad Yusof, Zaw Zaw
Published: (2014)

Classification of eukaryotic splice-junction genetic sequences using averaged one-dependence estimators with subsumption resolution
by: Htike@Muhammad Yusof, Zaw Zaw, et al.
Published: (2013)

Vehicle classification system using viola Jones and multi-layer perceptron
by: Saeed S, Almehmadi Tarig, et al.
Published: (2016)

Cortical activation during power grip task with pneumatic pressure gauge: an fMRI study
by: Mohamad, M., et al.
Published: (2017)

Auditory network connectivity in tinnitus patients: a resting-state fMRI study
by: Davies, J., et al.
Published: (2014)

Reducing the gradient artefact in simultaneous EEG-fMRI by adjusting the subject’s axial position
by: Mullinger, Karen J., et al.
Published: (2011)