Evidence map›Paper›PMID 42798415›Full record

ArticleFrontiers in artificial intelligence2026

NeuroTrustNet: a cost-effective multimodal ensemble framework for brain tumor classification under cross-dataset variability.

Ferdaus Ibne Aziz, Insoo Koo, Tumennast Erdenebold, Jubayer A Hossain, Asle Fagerstrøm, Debasish Ghose

Abstract read
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In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Ferdaus Ibne AzizDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea.
Insoo KooDepartment of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan, Republic of Korea.
Tumennast ErdeneboldAI and Big Data Department, Woosong University, Daejeon, Republic of Korea.
Jubayer A HossainFaculty of Nursing and Health Sciences, Nord University, Namsos, Norway.
Asle FagerstrømDepartment of Technology, Kristiania University of Applied Sciences, Bergen, Norway.
Debasish GhoseDepartment of Technology, Kristiania University of Applied Sciences, Bergen, Norway.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate brain tumor classification from Magnetic Resonance Imaging (MRI) remains challenging due to dataset heterogeneity, class imbalance, and limited interpretability, while practical deployment further requires models that balance predictive performance with computational efficiency. In this work, we propose

Indexed as

artificial intelligencebioinformatics and computational biologybrain tumor (BRAT)cancer biologyhealth informatics

Identifiers

PMID42798415
PMCPMC13612349

What OpenQuestion holds

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Registered trials

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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.