AIMC Topic: Brain Diseases

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Artificial intelligence techniques for neuropathological diagnostics and research.

Neuropathology : official journal of the Japanese Society of Neuropathology
Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neuron was written in a psychiatry department almost 80 years ago. However, the application of AI in dig...

SD-CNN: A static-dynamic convolutional neural network for functional brain networks.

Medical image analysis
Static functional connections (sFCs) and dynamic functional connections (dFCs) have been widely used in the resting-state functional MRI (rs-fMRI) analysis. sFCs, calculated based on entire rs-fMRI scans, can accurately describe the static topology o...

Multi-Level Functional Connectivity Fusion Classification Framework for Brain Disease Diagnosis.

IEEE journal of biomedical and health informatics
Brain disease diagnosis is a new hotspot in the cross research of artificial intelligence and neuroscience. Quantitative analysis of functional magnetic resonance imaging (fMRI) data can provide valuable biomarkers that contributes to clinical diagno...

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

NeuroImage
The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resources for understanding the neurobiological mechanisms underlying cognitive functions and brain disord...

A deep learning model to classify and detect brain abnormalities in portable microwave based imaging system.

Scientific reports
Automated classification and detection of brain abnormalities like a tumor(s) in reconstructed microwave (RMW) brain images are essential for medical application investigation and monitoring disease progression. This paper presents the automatic clas...

Convolutional Neural Network With Graphical Lasso to Extract Sparse Topological Features for Brain Disease Classification.

IEEE/ACM transactions on computational biology and bioinformatics
The functional connectivity provides new insights into the mechanisms of the human brain at network-level, which has been proved to be an effective biomarker for brain disease classification. Recently, machine learning methods have played an importan...

An Improved Brain MRI Classification Methodology Based on Statistical Features and Machine Learning Algorithms.

Computational and mathematical methods in medicine
In this paper, we have proposed a novel methodology based on statistical features and different machine learning algorithms. The proposed model can be divided into three main stages, namely, preprocessing, feature extraction, and classification. In t...

Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning.

Advances in biological regulation
Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to multiple mechanisms and exposure to diverse environments. These disorders further increase gene mutation...

Effect of combining features generated through non-linear analysis and wavelet transform of EEG signals for the diagnosis of encephalopathy.

Neuroscience letters
Electroencephalogram (EEG) signals portray hidden neuronal interactions in the brain and indicate brain dynamics. These signals are dynamic, complex, chaotic and nonlinear, the nature of which is represented with features - fractal dimensions, entrop...