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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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Related Experiment Video

Updated: Jun 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

Highly accurate brain tumor detection with high sensitivity using transform-based functions and machine learning

Ashish Bhatt, Vineeta Saxena Nigam

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine

|August 23, 2024

Summary

This summary is machine-generated.

This study introduces an advanced algorithm for accurate brain tumor detection using MRI scans. The method combines transform methods, hybrid optimization, and ensemble classification to achieve high detection accuracy.

Keywords:

Brain tumor feature fusion machine learning algorithm optimization transform function

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Last Updated: Jun 15, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Biology

Background:

Brain tumors present a significant global health challenge due to high mortality rates and complex imaging characteristics.
Accurate and early detection of brain tumors is critical for effective treatment planning and improved patient outcomes.
Existing detection algorithms face challenges in accurately classifying tumors due to variations in cell appearance and growth patterns.

Purpose of the Study:

To develop and validate a novel algorithm for enhanced brain tumor detection from MRI scans.
To improve the accuracy and reliability of automated brain tumor identification systems.
To address the limitations of current methods in feature extraction and classification.

Main Methods:

Utilized a combination of sub-band decomposition transform methods for texture feature extraction from MRI scans.
Employed hybrid feature optimization using firefly and glow-worm algorithms for effective feature selection.
Implemented MKSVM algorithm and a stacking ensemble classifier for robust brain tumor classification.

Main Results:

The proposed algorithm achieved high detection accuracy, sensitivity, and specificity, reaching up to 98%, 99%, and 99.5% respectively.
Validated performance using BRATS datasets (2013, 2015, 2018), demonstrating consistent effectiveness across different time periods.
Experimental results confirmed the algorithm's efficiency in detecting brain tumors.

Conclusions:

The study successfully developed an efficient brain tumor detection algorithm integrating advanced feature extraction and classification techniques.
The proposed method offers a significant advancement in automated medical image analysis for oncology.
The fusion of multiple feature extraction methods and ensemble classification proved effective for accurate brain tumor detection.