HybridFusionNet with Explanability: A Novel Explainable Deep Learning-Based Hybrid Framework for Enhanced Skin Lesion Classification Using Dermoscopic Images

Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations e... | Find, read and cite all the research you need on Tech Science Press

Authors

Publication Details

Journal
Computer Modeling in Engineering & Sciences
Published
2025-01-01
DOI
https://doi.org/10.32604/cmes.2025.072650
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
0.00
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article

HybridFusionNet with Explanability: A Novel Explainable Deep Learning-Based Hybrid Framework for Enhanced Skin Lesion Classification Using Dermoscopic Images

Mohammed A. ELAffendi, Mohamed Adel Hammad, Souham Meshoul
Computer Modeling in Engineering & Sciences
Cutaneous Melanoma Detection and Management
article

HybridFusionNet with Explanability: A Novel Explainable Deep Learning-Based Hybrid Framework for Enhanced Skin Lesion Classification Using Dermoscopic Images

Mohammed A. ELAffendi, Mohamed Adel Hammad, Souham Meshoul
article en

Abstract

Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations e... | Find, read and cite all the research you need on Tech Science Press

Computer Modeling in Engineering & SciencesVol. 145(1)
Openalex Percentile: Top 57%
Cutaneous Melanoma Detection and Management
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