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Related Experiment Video

Updated: Jul 17, 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

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.

B V Prakash¹, A Rajiv Kannan², N Santhiyakumari³

¹Faculty of Information Technology, Government College of Engineering, Erode, Tamil Nadu, India.

Scientific Reports

|September 4, 2023

Summary

This summary is machine-generated.

This study introduces a hybrid Convolutional Neural Network (HCNN) for automated meningioma brain tumor detection. The HCNN system achieves high accuracy in distinguishing tumors from non-tumors in medical images.

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Neuroscience

Background:

Meningioma detection is challenging due to low pixel intensity.
Automated systems are essential for modern medical platforms.
Accurate detection of brain tumors like meningiomas is critical.

Purpose of the Study:

To propose a novel hybrid Convolutional Neural Network (HCNN) for automated meningioma detection.
To develop an efficient HCNN classifier to differentiate meningioma from non-meningioma brain images.
To improve the accuracy and reliability of brain tumor detection systems.

Main Methods:

A hybrid Convolutional Neural Network (HCNN) classifier was developed.
The HCNN incorporates Ridgelet transform for pixel stability and feature extraction.
A segmentation algorithm was employed for precise tumor pixel identification.

Main Results:

The HCNN system demonstrated high performance on multiple datasets (BRATS 2019, Nanfang, BRATS 2022).
Achieved up to 99.81% classification accuracy and 99.8% segmentation accuracy.
Outperformed state-of-the-art meningioma detection algorithms in experimental comparisons.

Conclusions:

The proposed HCNN-based system offers a highly efficient and accurate solution for meningioma detection.
The integration of Ridgelet transform enhances feature stability and classification performance.
This automated approach holds significant potential for clinical application in neuro-oncology.