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

Nermeen Gamal Rezk, Samah Alshathri, Amged Sayed, Ezz El-Din Hemdan, Heba El-Behery

Abstract read

In one paragraph

Article in Diagnostics (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026
Review
A Robust Semi-Supervised Brain Tumor MRI Classification Network for Data-Constrained Clinical Environments.Diagnostics (Basel, Switzerland) · 2025
Article
Lightweight CNN for accurate brain tumor detection from MRI with limited training data.Frontiers in medicine · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Nermeen Gamal RezkDepartment of Computer and Systems Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Samah AlshathriDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.ORCID 0000-0002-8805-7890

Amged SayedDepartment of Electrical Energy Engineering, College of Engineering & Technology, Arab Academy for Science Technology & Maritime Transport, Smart Village Campus, Giza 12577, Egypt.ORCID 0000-0002-3114-2840

Ezz El-Din HemdanDepartment of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Menoufia 32952, Egypt.

Heba El-BeheryDepartment of Computer and Systems Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt.

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R197), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia PNURSP2024R197

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

brain tumor detectionchaotic and Arnold algorithmsdeep learningencrypted MRI imageshybrid modelsMedical IoTsecure diagnosis

Identifiers

PMID40075886

DOI10.3390/diagnostics15050639

PMCPMC11898425

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.