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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 22, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Chen Li¹, Faxue Zhang¹, Yongjian Du²

¹Department of Neurosurgery, Shandong Provincial Third Hospital, Shandong University, No.12 Wuyingshan Middle Road, Jinan, 250031, Shandong, China.

Scientific Reports

|July 2, 2024

Summary

This study introduces an intelligent method for brain tumor identification using magnetic resonance imaging (MRI) and deep learning. The novel approach achieves 98.6% accuracy in classifying tumor types from MRI scans.

Keywords:

Brain tumor Classification Convolutional neural network Firefly optimization MRI image segmentation

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Last Updated: Jun 22, 2025

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

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Published on: April 13, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Accurate brain tumor diagnosis from MRI scans is challenging for radiologists.
Identifying tumor types is crucial for effective treatment planning.
Current methods require improvement in accuracy and efficiency.

Purpose of the Study:

To develop an intelligent method for accurate brain tumor identification from MRI data.
To enhance the efficacy and accuracy of MRI-based tumor detection.
To investigate novel segmentation and classification techniques for brain tumors.

Main Methods:

Utilized convolutional neural networks (CNNs) for tumor classification.
Developed a novel segmentation technique based on firefly optimization (FFO).
Combined two CNN types for comprehensive tumor trait categorization.

Main Results:

Achieved an average accuracy of 98.6% in brain tumor identification.
The proposed FFO segmentation method demonstrated robust quality assessment.
The combined CNN approach effectively categorized tumor traits and identified tumor types.

Conclusions:

The intelligent method significantly improves brain tumor identification accuracy using MRI.
FFO-based segmentation and combined CNNs offer a promising approach for neuro-oncology.
This research enhances the diagnostic capabilities of MRI for brain tumor analysis.