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

Magnetic Resonance Imaging

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

You might also read

Related Articles

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

Sort by

Same author

Retraction Note: An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis.

Multimedia tools and applications·2026

Same author

An automated cloud-based system for in-situ geotechnical site characterization using cone penetration test (CPT/CPTu).

Scientific reports·2026

Same author

An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction.

Scientific reports·2026

Same author

An efficient IoT-based crop damage prediction framework in smart agricultural systems.

Scientific reports·2025

Same author

Explainable AI for Chronic Kidney Disease Prediction in Medical IoT: Integrating GANs and Few-Shot Learning.

Bioengineering (Basel, Switzerland)·2025

Same author

XAI-Augmented Voting Ensemble Models for Heart Disease Prediction: A SHAP and LIME-Based Approach.

Bioengineering (Basel, Switzerland)·2024

Search research articles

Related Experiment Video

Updated: May 22, 2025

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

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

Published on: November 30, 2022

Secure Hybrid Deep Learning for MRI-Based Brain Tumor Detection in Smart Medical IoT Systems.

Nermeen Gamal Rezk¹, Samah Alshathri², Amged Sayed^3,4

¹Department of Computer and Systems Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Diagnostics (Basel, Switzerland)

|March 13, 2025

Summary

This study introduces a secure method for classifying brain tumors using encrypted MRI images and deep learning. The approach ensures patient data privacy while maintaining high accuracy in tumor detection, crucial for early diagnosis in healthcare.

Keywords:

Medical IoT brain tumor detection chaotic and Arnold algorithms deep learning encrypted MRI images hybrid models secure diagnosis

More Related Videos

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

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Related Experiment Videos

Last Updated: May 22, 2025

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

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

Published on: November 30, 2022

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

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Cybersecurity

Background:

Brain tumors are aggressive, leading to high mortality rates.
Current diagnosis relies on invasive biopsies, often delayed until surgery.
Automated classification is needed to speed up diagnosis and reduce errors.

Purpose of the Study:

To develop a secure and automated system for brain tumor classification using MRI images.
To integrate encryption techniques with deep learning models for enhanced data security.
To ensure diagnostic accuracy is maintained even with encrypted medical data.

Main Methods:

Proposed a hybrid deep learning model combining VGG16 and a deep neural network (DNN).
Integrated chaotic and Arnold encryption techniques to secure MRI images.
Evaluated the classification performance on encrypted MRI datasets.

Main Results:

Achieved high classification accuracy with chaotic encryption (93.75%) and Arnold encryption (94.1%).
Demonstrated that encrypted images can be effectively classified without compromising diagnostic performance.
Reported precision, recall, and F-score values exceeding 93% for both encryption methods.

Conclusions:

The hybrid deep learning approach offers a secure and efficient solution for brain tumor detection in MIoT systems.
Encrypting MRI images before classification ensures patient data confidentiality.
This method supports early and secure diagnosis, empowering healthcare professionals globally.