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Related Experiment Video

Updated: Jun 28, 2026

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

Neurovision: A deep learning driven web application for brain tumour detection using weight-aware decision approach.

Thota Rishik Sai Santhosh¹, Sachi Nandan Mohanty¹, Nihar Ranjan Pradhan¹

¹School of Computer Science and Engineering (SCOPE), VIT-AP University, Inavolu, Amaravati, Andhra Pradesh, India.

|May 16, 2025

Summary

This summary is machine-generated.

This study introduces a novel deep learning framework for accurate brain tumor classification from MRI scans. The system uses a unique weight-aware mechanism to achieve high diagnostic accuracy, improving upon traditional methods.

Keywords:

Brain tumour classification MRI NeuroVision deep learning model weight-aware decision

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Last Updated: Jun 28, 2026

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

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Published on: April 13, 2013

Deep Neural Networks for Image-Based Dietary Assessment

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Computational Neuroscience

Background:

Accurate brain tumor diagnosis is critical but challenging due to the brain's complexity.
Existing classification methods may struggle with the nuances of medical resonance images.

Purpose of the Study:

To develop a robust deep learning framework for classifying brain tumors using medical resonance images.
To introduce a novel weight-aware decision mechanism for improved multi-class classification accuracy.

Main Methods:

Utilized four pre-trained deep learning models: DenseNet169, VGG-19, Xception, and EfficientNetV2B2.
Implemented a weight-aware decision module that aggregates predictions based on model-wise validation scores.
Trained and fine-tuned models on a training dataset, then evaluated on a test dataset.

Main Results:

Achieved high accuracy rates of 98.7%, 97.52%, and 94.94% on three distinct datasets.
The weight-aware mechanism effectively resolved tie situations in classification.
The framework demonstrated superior performance compared to conventional majority-based techniques.

Conclusions:

The developed deep learning framework offers a promising solution for accurate and efficient brain tumor classification.
The novel weight-aware decision mechanism enhances classification robustness.
The integrated web application provides convenient access for research and non-commercial use.