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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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A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

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CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Pallavi Tiwari¹, Bhaskar Pant¹, Mahmoud M Elarabawy²

¹Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, India.

Computational Intelligence and Neuroscience

|July 1, 2022

Summary

This summary is machine-generated.

This study introduces a new method for classifying brain tumors using Convolutional Neural Networks (CNNs). The model accurately identifies four types of brain tumors from MRI scans with 99% accuracy.

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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

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Published on: June 7, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors are a leading cause of death in adults and children, with varied survival rates based on type.
Accurate classification of brain tumors is critical for effective treatment and improved patient outcomes.
Multiclass classification is essential for grading and differentiating various brain tumor types.

Purpose of the Study:

To develop and evaluate a Convolutional Neural Network (CNN) model for accurate multiclass brain tumor classification.
To differentiate between no tumor, glioma, meningioma, and pituitary tumors using Magnetic Resonance Imaging (MRI).

Main Methods:

Utilized Magnetic Resonance Imaging (MRI) scans as the primary data source for brain imaging.
Implemented a Convolutional Neural Network (CNN) model, a leading approach in image classification, for tumor detection.
The model was trained to classify brain images into four distinct categories: no tumor, glioma, meningioma, and pituitary tumor.

Main Results:

The proposed CNN model achieved a high accuracy of 99% in classifying brain tumors.
Successfully differentiated between the four specified classes of brain conditions from MRI data.
Demonstrated the efficacy of CNNs in the complex task of brain tumor grading and identification.

Conclusions:

The developed CNN model offers a highly accurate and reliable method for brain tumor classification from MRI scans.
This advancement in AI-driven medical image analysis holds significant potential for improving diagnostic accuracy and patient care.
The study highlights the capability of deep learning techniques in addressing critical challenges in neuro-oncology.