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Related Experiment Video

Updated: May 10, 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

High-Performance Computing-Based Brain Tumor Detection Using Parallel Quantum Dilated Convolutional Neural Network.

Sushama Seetaram Shinde¹, Aparna Pande²

¹Department of Computer Science and Engineering, SunRise University, Alwar, Rajasthan, India.

NMR in Biomedicine

|April 21, 2025

Summary

This summary is machine-generated.

This study introduces a novel parallel quantum dilated convolutional neural network (PQDCNN) for accurate brain tumor detection using big data. The PQDCNN model significantly improves early diagnosis by overcoming limitations of existing magnetic resonance imaging methods.

Keywords:

Medav filter TransBTSV2 fuzzy local information C‐ means clustering map reducer parallel convolutional neural network quantum dilated convolutional neural network

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Last Updated: May 10, 2025

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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

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Neuroscience

Background:

Brain tumors are characterized by abnormal cell growth, necessitating accurate detection methods.
Magnetic Resonance Imaging (MRI) is crucial for identifying brain tumors, but current techniques face challenges with computational complexity, noise, and accuracy.
Early and precise diagnosis of brain tumors is critical for effective treatment and patient outcomes.

Purpose of the Study:

To propose a novel, high-performance computing approach for enhanced brain tumor detection.
To address the limitations of existing MRI analysis methods, including noise interference and low accuracy.
To develop and validate a parallel quantum dilated convolutional neural network (PQDCNN) for improved brain tumor identification.

Main Methods:

A big data-based detection model utilizing a parallel quantum dilated convolutional neural network (PQDCNN) with Map-Reducer architecture.
Data partitioning performed using Fuzzy Local Information C-Means clustering (FLICM).
Noise removal via Medav filtering, tumor segmentation using the TransBTSV2 transformer model, followed by image augmentation and feature extraction within the Map-Reducer framework.

Main Results:

The proposed PQDCNN model achieved high performance in brain tumor detection.
Validation metrics demonstrated excellent efficiency: 91.52% accuracy, 91.69% sensitivity, and 92.26% specificity.
The novel approach effectively overcomes computational complexity and noise issues prevalent in traditional methods.

Conclusions:

The developed PQDCNN model offers a significant advancement in brain tumor detection accuracy and efficiency.
This big data-driven approach enhances early diagnosis capabilities, potentially improving patient prognosis.
The integration of advanced AI techniques like PQDCNN and transformer models shows great promise for medical imaging analysis.