LHW-Net: An ensemble-based machine learning framework for brain tumor classification.
Overview
- Department of Computer Science and Engineering, SRM University AP, Amaravati, Guntur, Andhra Pradesh, India
- Department of Computer Science, College of Computer Science, King Khalid University, Abha, Kingdom of Saudi Arabia
Abstract
The classification of brain tumors is an unsolved problem associated with heterogeneity of tumors and fluctuations in imaging conditions. In this work, the investigation introduces a powerful novel framework, named LHW-Net that combines handcrafted features called local binary patterns (LBP), histogram of oriented gradients (HOG), and wavelet transform (WT). Within the LHW-Net framework, the extracted features are utilized in different machine learning classifiers, such as K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Classifier (SVC). The results of the individual classifiers are further combined using probabilistic score fusion approach to improve classification performance. The effectiveness and robustness of the proposed work are validated by the achieved experimental results on commonly accepted benchmark datasets.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
The paper links to its data, not to its authors' code: see the Data section.
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Data
Datasets cited
- figshare:1512427, at figshare; found in the references
- kaggle.com/
datasets/ , at Kaggle; found in the referencesahmedhamada0 - kaggle.com/
datasets/ , at Kaggle; found in the referencesdenizkavi1 - kaggle.com/
datasets/ , at Kaggle; found in “Data Availability”masoudnickparvar
Data Availability
All the data used in the publication are publicly available in repositories with following URL’s Brain Tumor MRI Dataset: https://
Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 29 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 8 MeSH terms, 1 funder, 34 references.
Cite
This paper
Suryadevara, T., Mahamkali, N., & Rafi, M. (2026). LHW-Net: An ensemble-based machine learning framework for brain tumor classification. PloS one, 21(4), e0346821. https://
BibTeX
@article{suryadevara2026
author = {Suryadevara, Thireesha and Mahamkali, Naveenkumar and Rafi, Mudassir},
title = {{LHW-Net: An ensemble-based machine learning framework for brain tumor classification}},
journal = {PloS one},
year = {2026},
month = apr,
volume = {21},
number = {4},
pages = {e0346821},
publisher = {PLOS},
issn = {1932-6203},
doi = {10.1371/
url = {https://
pmid = {42013111},
pmcid = {PMC13098932}
}
RIS
TY - JOUR
AU - Suryadevara, Thireesha
AU - Mahamkali, Naveenkumar
AU - Rafi, Mudassir
TI - LHW-Net: An ensemble-based machine learning framework for brain tumor classification
T2 - PloS one
J2 - PLoS One
PY - 2026
DA - 2026/
VL - 21
IS - 4
SP - e0346821
SN - 1932-6203
PB - PLOS
DO - 10.1371/
UR - https://
LA - en
ER -
CSL-JSON
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