Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy

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Publication Details

Journal
Journal of Medical Signals & Sensors
Published
2026-02-01
DOI
https://doi.org/10.4103/jmss.jmss_74_24
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
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article

Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy

Hossein Ebrahimnezhad, Nasrin Rahmani
Journal of Medical Signals & Sensors
Cutaneous Melanoma Detection and Management
article

Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy

Hossein Ebrahimnezhad, Nasrin Rahmani
article en

Abstract

No abstract available for this paper.
Journal of Medical Signals & SensorsVol. 16(2)
Sahand University of Technology (IR)
Openalex Percentile: Top 71%
Cutaneous Melanoma Detection and Management
DOIOpen Access PDFOpenAlex
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Improving Skin Lesion Diagnosis: A Hybrid Approach Using Orthogonal Combination of Local Binary Pattern Features and Ensemble Learning for Diagnostic Accuracy — Hossein Ebrahimnezhad, Nasrin Rahmani · Journal of Medical Signals & Sensors (2026) | TGRS Research Map | TGRS