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Related Experiment Video

Updated: Jun 9, 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

Utilizing customized CNN for brain tumor prediction with explainable AI.

Md Imran Nazir¹, Afsana Akter¹, Md Anwar Hussen Wadud²

¹Department of Computer Science & Engineering, Bangladesh University of Business & Technology, Dhaka, Bangladesh.

|October 25, 2024

Summary

This summary is machine-generated.

This study introduces a transparent AI model for brain tumor detection using MRI scans. The explainable AI approach achieves high accuracy, improving diagnostic trust and patient outcomes.

Keywords:

Brain tumor CNN Diagonostic Explainable AI Grad-Cam LIME MRI SHAP

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

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Last Updated: Jun 9, 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
Oncology

Background:

Timely brain tumor diagnosis via MRI is crucial for patient survival.
Traditional deep learning (DL) models lack transparency, hindering medical expert trust.
The
black box
nature of DL models creates skepticism in clinical applications.

Purpose of the Study:

To develop an innovative and transparent AI approach for accurate brain tumor detection using MRI.
To enhance trust in AI-driven medical diagnostics through explainable AI (XAI).
To improve early tumor detection rates and potentially patient survival.

Main Methods:

Utilized a customized Convolutional Neural Network (CNN) model for brain tumor detection.
Integrated three advanced explainable artificial intelligence (XAI) techniques: SHAP, LIME, and Grad-CAM.
Trained and validated the model on the BR35H dataset comprising 3060 brain MRI images.

Main Results:

Achieved 100% training accuracy and 98.67% validation accuracy.
Demonstrated exceptional performance with 98.50% for precision, recall, and F1 score.
Outperformed existing models in accuracy and generalizability tests.

Conclusions:

The proposed CNN model integrated with XAI techniques offers a reliable and transparent solution for brain tumor detection.
This approach enhances trust in AI for medical diagnostics, paving the way for early interventions.
Sets a new benchmark for accuracy in AI-assisted brain tumor diagnosis using MRI.