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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.
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Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

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A fine-tuned vision transformer based enhanced multi-class brain tumor classification using MRI scan imagery.

C Kishor Kumar Reddy¹, Pulakurthi Anaghaa Reddy¹, Himaja Janapati¹

¹Department of Computer Science and Engineering, Stanley College of Engineering and Technology for Women, Hyderabad, India.

Frontiers in Oncology

|August 2, 2024

Summary

This study shows Fine-Tuned Vision Transformer models (FTVTs) excel at classifying brain tumors from MRI scans. The FTVT-l16 model achieved the highest accuracy, demonstrating their effectiveness in medical image analysis.

Keywords:

FTVT MRI scans deep learning models medical image processing vision transformers

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Last Updated: Jun 18, 2025

Role of Diffusion MRI Tractography in Endoscopic Endonasal Skull Base Surgery

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Published on: July 5, 2021

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

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Published on: June 7, 2020

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

Area of Science:

Medical Image Analysis
Artificial Intelligence in Healthcare
Oncology

Background:

Brain tumors, abnormal cell growths, require early detection for effective treatment.
Magnetic Resonance Imaging (MRI) is crucial for brain tumor diagnosis.
Deep learning models have shown promise in analyzing medical images.

Purpose of the Study:

To investigate the efficacy of novel Fine-Tuned Vision Transformer models (FTVTs) for brain tumor classification.
To compare FTVTs against established deep learning models like ResNet50, MobileNet-V2, and EfficientNet-B0.
To evaluate model performance using accuracy, recall, precision, and F1-score.

Main Methods:

Utilized a dataset of 7,023 MRI scans categorized into glioma, meningioma, pituitary, and no tumor.
Implemented and compared four FTVT models (FTVT-b16, FTVT-b32, FTVT-l16, FTVT-l32).
Benchmarked FTVTs against ResNet50, MobileNet-V2, and EfficientNet-B0.

Main Results:

FTVT models demonstrated superior performance in brain tumor classification.
The FTVT-l16 model achieved the highest accuracy of 98.70%.
Other FTVT models also showed high accuracies (96.87%–98.62%), outperforming established models.

Conclusions:

Fine-Tuned Vision Transformer models are highly effective and robust for brain tumor classification using MRI data.
FTVTs represent a significant advancement in AI-driven medical image processing for oncology.
The study highlights the potential of FTVTs for improving diagnostic accuracy in neuroimaging.