Benchmark of twelve models finds hybrid CNN-transformer architectures and a SigLIP vision-language model deliver the strongest overall performance on skin cancer detection using the PAD-UFES-20 dataset.
Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features
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abstract
This paper provides the required description of the methods used to obtain submitted results for Task1 and Task 3 of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. The results have been created by a team of researchers at the University of Dayton Signal and Image Processing Lab. In this submission, traditional classifiers with hand-crafted features are utilized for Task 1 and Task 3. Our team is providing additional separate submissions using deep learning methods for comparison.
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CNNs, Transformers, Hybrid, and Vision Language Models for Skin Cancer Detection
Benchmark of twelve models finds hybrid CNN-transformer architectures and a SigLIP vision-language model deliver the strongest overall performance on skin cancer detection using the PAD-UFES-20 dataset.