ArticleInternational journal of breast cancer2025
Enhancing Skin Cancer Diagnosis Through Fine-Tuning of Pretrained Models: A Two-Phase Transfer Learning Approach.
Article in International journal of breast cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- R3MV: a novel reliable system architecture for skin cancer classification using progressive heterogeneous multiblock model.Scientific reports · 2026Article
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Abstract
Skin cancer is among the most prevalent types of cancer worldwide, and early detection is crucial for improving treatment outcomes and patient survival rates. Traditional diagnostic methods, often reliant on visual examination and manual evaluation, can be subjective and time-consuming, leading to variability in accuracy. Recent developments in machine learning, particularly using pretrained models and fine-tuning techniques, offer promising advancements in automating and improving skin cancer classification. This paper explores the application of a two-phase model using the HAM10000 dataset, which comprises a wide range of skin lesion images. The first phase employs transfer learning with frozen layers, followed by fine-tuning all layers in the second phase to adapt the models more specifically to the dataset. I evaluate nine pretrained models, including VGG16, VGG19, InceptionV3, Xception (extreme inception), and DenseNet121, assessing their performance based on accuracy, precision, recall, and
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