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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Brain Disorder Detection and Diagnosis using Machine Learning and Deep Learning - A Bibliometric Analysis.

Jyotismita Chaki¹, Gopikrishna Deshpande^{2,3,4,5,6,7,8,9,10}

¹School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India.

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Summary

This study analyzes machine learning and deep learning for brain disorder detection. Research is rapidly growing, with a focus on Alzheimer's, autism, and Parkinson's disease, highlighting key trends and future research directions.

Keywords:

Alzheimer’s Brain disorder Parkinson’s autism.deep learning machine learning

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Neurology
Computer Science
Medical Informatics

Background:

Brain disorders represent a significant global mortality challenge, underscoring the critical need for early detection and diagnosis.
Machine learning (ML) and deep learning (DL) are emerging as powerful tools for identifying and diagnosing neurological conditions.
A comprehensive bibliometric analysis is essential to map the evolving landscape of ML/DL applications in brain disorder research.

Purpose of the Study:

To conduct a quantitative bibliometric analysis of research on machine learning and deep learning for brain disorder detection and diagnosis.
To identify key trends, influential works, and collaborative patterns within this rapidly advancing field.
To provide insights that can guide future research directions and accelerate progress in the application of AI for neurological health.

Main Methods:

A bibliometric analysis was performed on 1550 articles published between 2015 and May 2023, sourced from the Scopus database.
Data analysis utilized Biblioshiny and VOSviewer platforms to examine citation patterns, collaboration metrics, and keyword frequencies.
The study focused on automated detection and diagnosis of brain disorders using ML and DL techniques.

Main Results:

Research output has shown a consistent upward trend, peaking in 2022.
Multiclass classification and convolutional neural network models are dominant approaches.
Alzheimer's disease, autism, and Parkinson's disease are the most frequently studied conditions.
The USA, China, and India are leading countries in terms of author and institute collaborations.

Conclusions:

This bibliometric analysis offers valuable insights into the current trends in ML and DL for brain disorder detection.
The findings highlight key areas of focus and identify opportunities for future research and development.
The study provides a roadmap for researchers to advance the application of AI in diagnosing and managing brain disorders.