Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Short-Term Outcomes of Augmentation Urethroplasty in Recurrent Urethral Stricture Following Repeated Failed Optical Internal Urethrotomy: A Prospective Study.

Cureus·2026

Same author

Diagnostic Utility of Extended Focused Assessment With Sonography in Trauma (eFAST) in Hemodynamically Stable Patients With Blunt Thoracoabdominal Injury: A Prospective Study.

Cureus·2026

Same author

Cost-effective mitigation strategies for air quality management in Uttar Pradesh, India.

Journal of environmental management·2026

Same author

A review on AI-enabled drug design in medicinal chemistry: Analytical validation, challenges, and regulatory considerations.

Talanta·2026

Same author

Evolving Aerobic Bacterial Skin Flora and Healthcare-Associated Infections Among Intensive Care Unit Patients at a Tertiary Care Hospital: A Therapeutic Concern.

Cureus·2026

Same author

Nanoparticle immunoimaging reveals metabolic dysfunction fueling CCR2-dependent inflammation in infarcted hearts.

Journal of controlled release : official journal of the Controlled Release Society·2025

Search research articles

Related Experiment Video

Updated: May 2, 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

Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its

Gopal Singh Tandel¹, Ashish Tiwari², Omprakash G Kakde²

¹Department of Computer Science, Allahabad Degree College, University of Allahabad, Prayagraj, India. gtandel@gmail.com.

Journal of Imaging Informatics in Medicine

|January 8, 2025

Summary

This study introduces a non-invasive MRI-based tool using ensemble deep learning to accurately grade brain tumors. The developed system significantly outperforms traditional methods, offering a reliable alternative to invasive biopsies for tumor diagnosis.

Keywords:

Brain tumor Classification Deep learning Ensemble Explainable AI Magnetic resonance imaging Transfer learning

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

Related Experiment Videos

Last Updated: May 2, 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

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

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

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumor diagnosis relies on invasive biopsies, posing risks and leading to analysis inconsistencies.
Current diagnostic methods lack cost-effectiveness and swiftness in identifying tumor grades.

Purpose of the Study:

To develop a non-invasive, cost-effective, MRI-based computer-aided diagnosis tool for reliable and swift brain tumor grading.
To enhance diagnostic accuracy and patient safety by reducing reliance on invasive procedures.

Main Methods:

An ensemble deep learning (EDL) framework was developed using a majority voting algorithm.
The EDL system integrated seven deep learning models and seven machine learning models for multiclass brain tumor classification across five datasets (C2-C6).
Local Interpretable Model-Agnostic Explanations (LIME) were used for explainable AI, visualizing model decision-making processes.

Main Results:

The DL-based majority voting algorithm achieved superior performance over the ML-based approach.
Highest average accuracies were recorded: 100% (C2), 98.55% (C3), 98.47% (C4), 95.34% (C5), and 96.61% (C6).
Majority voting demonstrated consistent results and enhanced performance compared to individual models.

Conclusions:

The developed MRI-based EDL system offers a highly accurate and reliable non-invasive method for brain tumor grading.
This approach provides a cost-effective and swift alternative to traditional biopsy methods.
The use of LIME enhances the credibility and interpretability of the AI-driven diagnostic tool.