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A Transfer Learning Approach for Skin Cancer Classification Using Dense CNN Optimized with RAdam and CosineAnnealing

2026-04-09

Abstract excerpt

Skin cancer is one of the most life-threatening diseases in humans, but early and accurate detection can drastically improve patient outcomes. In this work, we present a comprehensive benchmarking and optimization study of stateof-the-art deep learning models for dermatoscopic skin lesions classification. We evaluated multiple CNN architectures, including VGG variants (VGG11, VGG13, VGG16, VGG19), ResNet18, ResNet...

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Literature Corpus work
8e3e0183-ecdd-5ccb-857c-58d4e7642511
DOI
10.22541/au.177575361.19221342/v1
Open publication

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A Transfer Learning Approach for Skin Cancer Classification Using Dense CNN Optimized with RAdam and CosineAnnealingDOI 10.22541/au.177575361.19221342/v1
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