AIMC Topic: Brain

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Deep Learning AI Applications in the Imaging of Glioma.

Topics in magnetic resonance imaging : TMRI
This manuscript will review emerging applications of artificial intelligence, specifically deep learning, and its application to glioblastoma multiforme (GBM), the most common primary malignant brain tumor. Current deep learning approaches, commonly ...

Atlas-Based Classification Algorithms for Identification of Informative Brain Regions in fMRI Data.

Neuroinformatics
Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to differe...

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Journal of integrative neuroscience
Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain activity patterns and their correspondence between symptoms and diseases, it is possible to give an accu...

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Brain : a journal of neurology
Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a multi-institutional multi-ethnic cohort, using novel semi-supervised machine learning methods designed t...

Dynamic changes of views on the brain changes of Cushing's syndrome using different computer-assisted tool.

Reviews in endocrine & metabolic disorders
Cushing's syndrome (CS) provides a unique model for assessing the neurotoxic effect of chronic hypercortisolism on human brains. With the ongoing development of different computer-assisted tools, four research stages emerged, each with its own pearls...

Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.

Neuron
Evolution is a blind fitting process by which organisms become adapted to their environment. Does the brain use similar brute-force fitting processes to learn how to perceive and act upon the world? Recent advances in artificial neural networks have ...

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images.

Current medical imaging
BACKGROUND: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review ...

SDResU-Net: Separable and Dilated Residual U-Net for MRI Brain Tumor Segmentation.

Current medical imaging
BACKGROUND: Glioma is one of the most common and aggressive primary brain tumors that endanger human health. Tumors segmentation is a key step in assisting the diagnosis and treatment of cancer disease. However, it is a relatively challenging task to...

A natural evolution optimization based deep learning algorithm for neurological disorder classification.

Bio-medical materials and engineering
BACKGROUND: A neurological disorder is one of the significant problems of the nervous system that affects the essential functions of the human brain and spinal cord. Monitoring brain activity through electroencephalography (EEG) has become an importa...