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Related Experiment Video

Updated: Jul 19, 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

An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.

Saswati Sahoo¹, Sushruta Mishra¹, Baidyanath Panda²

¹School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, India.

Sensors (Basel, Switzerland)

|August 12, 2023

Summary

This summary is machine-generated.

Early brain tumor detection is improved using a novel hybrid model. Progressive-growing generative adversarial networks (PGGAN) with a modulated convolution neural network (CNN) achieved 98.85% accuracy, enhancing clinical diagnosis.

Keywords:

PGGAN brain tumor deep learning generative adversarial network machine learning soft voting

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Early brain tumor detection is challenging, with current machine learning models lacking sufficient accuracy and speed.
Accurate and timely diagnosis is crucial for effective brain tumor treatment and patient outcomes.

Purpose of the Study:

To analyze the performance of different generative adversarial networks (GANs) for early brain tumor detection.
To propose a novel hybrid enhanced predictive convolution neural network (CNN) model for improved brain tumor screening.

Main Methods:

A hybrid GAN ensemble was used to augment brain tumor image data.
A hybrid modulated CNN technique processed the augmented data for classification.
A soft voting approach determined the final prediction based on GAN performance metrics.

Main Results:

The progressive-growing generative adversarial network (PGGAN) architecture demonstrated superior performance.
PGGAN achieved high accuracy (98.85%), precision (98.45%), recall (97.2%), F1-score (98.11%), and NPV (98.09%).
The PGGAN model exhibited low latency (3.4 s) for real-time identification of brain tissues.

Conclusions:

The proposed PGGAN augmentation with the modulated CNN technique offers optimal performance for brain tumor detection.
This hybrid approach enhances the reliability and efficiency of early-stage brain tumor assessment.
The findings suggest a promising tool for clinicians to aid in early and accurate brain tumor diagnosis.