Evidence map›Paper›PMID 35310201›Full record

ArticleFrontiers in psychology2022

MRI Brain Tumor Image Classification Using a Combined Feature and Image-Based Classifier.

A Veeramuthu, S Meenakshi, G Mathivanan, Ketan Kotecha, Jatinderkumar R Saini, V Vijayakumar, V Subramaniyaswamy

Abstract read
In one paragraph

Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

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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

7 authors.

A VeeramuthuDepartment of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.
S MeenakshiDepartment of Information Technology, Jeppiaar SRR Engineering College, Chennai, India.
G MathivananDepartment of Information Technology, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India.
Ketan KotechaSymbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune, India.
Jatinderkumar R SainiSymbiosis Institute of Computer Studies and Research, Symbiosis International (Deemed University), Pune, India.
V VijayakumarSchool of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
V SubramaniyaswamySchool of Computing, Shanmugha Arts, Science, Technology & Research Academy Deemed University, Thanjavur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain tumor classification plays a niche role in medical prognosis and effective treatment process. We have proposed a combined feature and image-based classifier (CFIC) for brain tumor image classification in this study. Carious deep neural network and deep convolutional neural networks (DCNN)-based architectures are proposed for image classification, namely, actual image feature-based classifier (AIFC), segmented image feature-based classifier (SIFC), actual and segmented image feature-based classifier (ASIFC), actual image-based classifier (AIC), segmented image-based classifier (

Indexed as

actual imagebrain tumorclassificationcombined feature and image based classifier (CFIC)deep neural networksegmented image

Identifiers

PMID35310201
PMCPMC8931531

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.