The rise in dermatological conditions, especially skin cancers, highlights the urgency for accurate diagnostics. Traditional imaging methods face challenges in capturing complex skin lesion patterns, risking misdiagnoses. Classical CNNs, though effective, often miss intricate patterns and contextual nuances.

Materials and Methods

Our research investigates the adoption of Vision Transformers (ViTs) in diagnosing skin lesions, capitalizing on their attention mechanisms and global contextual insights. Utilizing the fictional Dermatological Vision Dataset (DermVisD) with over 15,000 annotated images, we compare ViTs against traditional CNNs. This approach aims to assess the potential benefits of ViTs in dermatology.


Initial experiments showcase an 18% improvement in diagnostic accuracy using ViTs over CNNs, with ViTs achieving a remarkable 97.8% accuracy on the validation set. These findings suggest that ViTs are significantly more adept at recognizing complex lesion patterns.


The integration of Vision Transformers into dermatological imaging marks a promising shift towards more accurate diagnostics. By leveraging global contextual understanding and attention mechanisms, ViTs offer a nuanced approach that could surpass traditional methods. This advancement indicates a potential for setting new accuracy benchmarks in skin lesion diagnostics.


ViTs present a significant advancement in the field of dermatological imaging, potentially redefining accuracy and reliability standards. This study underscores the transformative impact of ViTs on the detection and diagnosis of skin conditions, advocating for their broader adoption in clinical settings.

Keywords: Vision transformers (ViTs), Skin lesion diagnostics, Deep learning, Medical image analysis, Human and disease, Health system.
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