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Related Experiment Video

Updated: Sep 6, 2025

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

A Hybrid Deep Learning Model for Brain Tumour Classification.

Mohammed Rasool¹, Nor Azman Ismail¹, Wadii Boulila²

¹School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai, Johor Bahru 81310, Malaysia.

Entropy (Basel, Switzerland)

|June 24, 2022

Summary

This summary is machine-generated.

This study introduces a hybrid deep learning model for brain tumor classification using MRI images. The Google-Net with SVM approach achieved 98.1% accuracy, improving early tumor detection.

Keywords:

CNN Google-Net MRI images SVM brain tumour deep learning fine-tuning

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Last Updated: Sep 6, 2025

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumors are a leading cause of death, with early detection crucial for treatability.
Current classification relies on invasive biopsies, often post-surgery.
Manual radiological diagnosis can be prone to errors and delays.

Purpose of the Study:

To develop an automated, non-invasive image classification technique for brain tumors.
To accelerate diagnosis and improve accuracy in brain tumor detection.
To assist radiologists in identifying glioma, meningioma, and pituitary tumors using MRI.

Main Methods:

A hybrid Convolutional Neural Network (CNN) architecture was developed.
Two classification methods were explored: Google-Net with Support Vector Machine (SVM) and finely tuned Google-Net with SoftMax.
The models were trained and validated on a dataset of 3460 MRI brain images (glioma, meningioma, pituitary tumors, and normal scans).

Main Results:

The finely tuned Google-Net with SoftMax achieved 93.1% accuracy.
The hybrid approach combining Google-Net feature extraction with SVM classification reached a superior accuracy of 98.1%.
This demonstrates the effectiveness of the hybrid model in classifying different brain tumor types.

Conclusions:

Hybrid deep learning models, particularly Google-Net with SVM, offer high accuracy for non-invasive brain tumor classification.
This technology can significantly aid radiologists, reduce diagnostic errors, and expedite treatment.
Automated MRI-based classification holds promise for improving patient outcomes in neuro-oncology.