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Related Experiment Video

Updated: Dec 15, 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

Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm.

Gopal S Tandel¹, Antonella Balestrieri², Tanay Jujaray³

¹Department of Computer Science and Engineering, VNIT, Nagpur, India.

Computers in Biology and Medicine

|July 14, 2020

Summary

A new artificial intelligence (AI) system using Convolutional Neural Network (CNN) accurately grades brain tumors from MRI scans. This non-invasive AI tool outperforms traditional machine learning methods for faster and more precise tumor classification.

Keywords:

Artificial intelligence Benchmarking Classification Convolution neural network Machine learning Performance Transfer learning Tumour grading system Validation Verification

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Published on: October 10, 2018

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Oncology
Computational Neuroscience

Background:

Brain tumors are a significant cause of mortality, with metastasis from other cancers being a common pathway.
Traditional biopsy methods for diagnosis present challenges including low accuracy, procedural risks, and lengthy result times.
There is a critical need for automated, non-invasive diagnostic tools for rapid and accurate brain tumor grading.

Purpose of the Study:

To develop and evaluate a non-invasive, automated computer-aided diagnosis tool for brain tumor grading.
To assess the performance of a transfer-learning-based Artificial Intelligence (AI) paradigm using Convolutional Neural Network (CNN) for brain tumor classification.
To compare the efficacy of the proposed AI model against traditional machine learning (ML) methods.

Main Methods:

Development of five clinically relevant multiclass datasets (2- to 6-class) for brain tumor grading.
Implementation of a transfer-learning-based CNN model for analyzing magnetic resonance imaging (MRI) data.
Benchmarking the CNN model against six ML classifiers: Decision Tree, Linear Discrimination, Naive Bayes, Support Vector Machine, K-nearest neighbour, and Ensemble.

Main Results:

The CNN-based deep learning (DL) model significantly outperformed all six ML models across all multiclass datasets.
The CNN-based AlexNet transfer learning system achieved high mean accuracies (e.g., 100% for 2-class, 95.97% for 3-class) across cross-validation protocols.
The DL model demonstrated a 12.12% improvement in mean area under the curve (0.99 vs. 0.87) compared to ML models (p < 0.0001).

Conclusions:

The transfer-learning-based AI system, utilizing CNNs, is highly effective for multiclass brain tumor grading.
The proposed AI approach offers superior performance compared to conventional ML systems for brain tumor classification.
This AI system provides a promising non-invasive alternative for accurate and rapid brain tumor diagnosis.