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Related Experiment Video

Updated: Jul 14, 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

Multimodal hybrid convolutional neural network based brain tumor grade classification.

A Rohini¹, Carol Praveen², Sandeep Kumar Mathivanan³

¹Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, Andhra Pradesh, 531162, India.

BMC Bioinformatics

|October 10, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning model using VGG-19 for accurate brain tumor detection. The enhanced method achieves high accuracy, offering a faster and more reliable alternative to traditional diagnostic procedures.

Keywords:

Customized CNN Deep learning Magnetic resonance image Transfer learning Tumor classification VGG19

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Last Updated: Jul 14, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Neuroscience

Background:

Brain tumors are abnormal cell growths that can be cancerous or benign, posing significant health risks.
Current diagnostic methods like CT and MRI are labor-intensive and can be inaccurate.
There is a need for efficient, accurate, and cost-effective brain tumor diagnostic tools.

Purpose of the Study:

To develop an automated deep learning model for accurate brain tumor identification.
To leverage transfer learning and convolutional neural networks for enhanced diagnostic performance.
To provide a faster and more reliable alternative to conventional brain tumor detection methods.

Main Methods:

Utilized transfer learning with the pre-trained VGG-19 model.
Implemented a customized convolutional neural network (CNN) framework.
Applied pre-processing techniques: normalization and data augmentation.
Trained and tested the model on a dataset of 407 CT images (257 tumor, 150 non-tumor).

Main Results:

Achieved a remarkable accuracy rate of 99.43%.
Demonstrated high sensitivity at 98.73% and specificity at 97.21%.
The model showed superior performance compared to traditional diagnostic methods.

Conclusions:

The proposed deep learning model offers a highly accurate and efficient solution for brain tumor detection.
This AI-driven approach can significantly aid in the early and reliable diagnosis of brain tumors from CT images.
The findings suggest potential for clinical application in developing advanced diagnostic tools.