Evidence map›Paper›PMID 38468053›Full record

ArticleAdvances in neurobiology2024

Percolation Images: Fractal Geometry Features for Brain Tumor Classification.

Alessandra Lumini, Guilherme Freire Roberto, Leandro Alves Neves, Alessandro Santana Martins, Marcelo Zanchetta do Nascimento

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Article in Advances in neurobiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

5 authors.

Alessandra LuminiDepartment of Computer Science and Engineering, University of Bologna, Cesena, FC, Italy. alessandra.lumini@unibo.it.
Guilherme Freire RobertoInstitute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), São Carlos, SP, Brazil.
Leandro Alves NevesDepartment of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), São José do Rio Preto, SP, Brazil.
Alessandro Santana MartinsFederal Institute of Triângulo Mineiro (IFTM), Ituiutaba, MG, Brazil.
Marcelo Zanchetta do NascimentoFaculty of Computation (FACOM), Federal University of Uberlândia (UFU), Uberlândia, MG, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain tumor detection is crucial for clinical diagnosis and efficient therapy. In this work, we propose a hybrid approach for brain tumor classification based on both fractal geometry features and deep learning. In our proposed framework, we adopt the concept of fractal geometry to generate a "percolation" image with the aim of highlighting important spatial properties in brain images. Then both the original and the percolation images are provided as input to a convolutional neural network to detect the tumor. Extensive experiments, carried out on a well-known benchmark dataset, indicate that using percolation images can help the system perform better.

Indexed as

Brain NeoplasmsFractalsBrainHumansNeural Networks, ComputerBrain tumorsClassification ensembleDeep learningFeature representationsFractal features

Identifiers

PMID38468053

What OpenQuestion holds

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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.