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Brain Diseases

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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...

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

Computational and mathematical methods in medicine
We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach...

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...

Learning the impact of acute and chronic diseases on forecasting neonatal encephalopathy.

Computer methods and programs in biomedicine
OBJECTIVE: There is a wide range of risk factors predisposing to the onset of neonatal encephalopathy (NE), including maternal antepartum/intrapartum comorbidities or events. However, few studies have investigated the difference in the impact of acut...

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...

Clinical Artificial Intelligence Applications in Radiology: Neuro.

Radiologic clinics of North America
Radiologists have been at the forefront of the digitization process in medicine. Artificial intelligence (AI) is a promising area of innovation, particularly in medical imaging. The number of applications of AI in neuroradiology has also grown. This ...

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...

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...

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...