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Related Experiment Video

Updated: Jul 5, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

A hybrid deep CNN model for brain tumor image multi-classification.

Saravanan Srinivasan¹, Divya Francis², Sandeep Kumar Mathivanan³

¹Department of Computer Science and Engineering, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India.

BMC Medical Imaging

|January 19, 2024

Summary

This study introduces a deep convolutional neural network (CNN) system for automated brain tumor classification. The novel CNN models achieve high accuracy in detecting, typing, and grading brain tumors, improving upon traditional methods.

Keywords:

Brain tumor grading Grid search Hybrid convolutional neural network Hybrid deep learning Hyperparameters

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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Histological evaluation of biopsy samples is the current standard for brain tumor diagnosis and classification.
This traditional method is invasive, time-consuming, and prone to manual errors, highlighting the need for advanced automated systems.

Purpose of the Study:

To develop and evaluate a fully automated, deep-learning-based multi-classification system for brain malignancies using deep convolutional neural networks (CNNs).
To enhance the early detection and accurate classification of brain tumors.

Main Methods:

Development of three distinct CNN models tailored for specific classification tasks: tumor detection, multi-type classification (normal, glioma, meningioma, pituitary, metastatic), and tumor grading.
Utilized a grid search optimization approach for automatic hyperparameter tuning of the CNN models.
Trained and validated models on large, publicly accessible clinical datasets.

Main Results:

The first CNN model achieved 99.53% accuracy for brain tumor detection.
The second CNN model accurately classified tumors into five types with 93.81% accuracy.
The third CNN model achieved 98.56% accuracy in classifying tumor grades.
The proposed deep CNN models outperformed classical models like AlexNet, DenseNet121, ResNet-101, VGG-19, and GoogleNet.

Conclusions:

The developed deep CNN-based system offers a superior, automated approach to brain tumor classification compared to traditional methods.
The models demonstrate high accuracy and reliability, paving the way for improved early detection and diagnosis of brain malignancies.
This research significantly advances the field of automated brain tumor analysis through deep learning.