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

A Transfer Learning Approach for Skin Cancer Classification Using Dense CNN Optimized with RAdam and CosineAnnealing | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 April 2026 V1 Latest version Share on A Transfer Learning Approach for Skin Cancer Classification Using Dense CNN Optimized with RAdam and CosineAnnealing Authors : Arka Goswami 0009-0009-9701-7274 [email protected] , Koushani Chandra , and Subhajit Kar Authors Info & Affiliations https://doi.org/10.22541/au.177575361.19221342/v1 54 views 31 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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, ResNet34, MobileNetV2, DenseNet121, and EfficientNetB0. To push performance boundaries, we integrate cutting-edge optimization techniques such as RAdam, Lookahead, and Ranger, along with advanced architectures like Vision Transformers (ViT with Folds), DenseNet with AlphaTensor-enhanced training, and Self-Distilling ConvNeXt combined with 5-Fold Ensembling. Our experiments demonstrate that a fine-tuned DenseNet model with hybrid optimizers and AlphaTensor-driven enhancements achieves a state-of-the-art validation accuracy of 91.67 percent, significantly outperforming conventional pipelines. This study provides a comprehensive comparative framework and highlights novel strategies for building next-generation AI-driven diagnostic systems for the detection of skin cancer. Supplementary Material File (a_transfer_learning_approach_for_skin_cancer_classification_using_dense_cnn_optimized_with_radam_and_cosineannealing.pdf) Download 1.62 MB Information & Authors Information Version history V1 Version 1 09 April 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords con-vnext deep learning densenet dermatoscopic classification ensembling optimizers skin cancer vision transformer Authors Affiliations Arka Goswami 0009-0009-9701-7274 [email protected] Department of Electrical Engineering Institute of Engg.& Management, University of Engg.& Management Kolkata View all articles by this author Koushani Chandra Department of CSE AIML Institute of Engg.& Management University of Engg.& Management Kolkata View all articles by this author Subhajit Kar Department of Electrical Engineering Institute of Engg.& Management University of Engg.& Management Kolkata View all articles by this author Metrics & Citations Metrics Article Usage 54 views 31 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Arka Goswami, Koushani Chandra, Subhajit Kar. A Transfer Learning Approach for Skin Cancer Classification Using Dense CNN Optimized with RAdam and CosineAnnealing. Authorea . 09 April 2026. 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