A significant obstacle to early skin cancer detection is the diversity of skin types and colors. Compared to convolutional neural networks (CNNs), Vision Transformers (ViTs) are a powerful alternative. By applying self-attention mechanisms to model global dependencies within an image, ViTs can capture long-range contextual information more effectively than traditional CNNs that use local convolutions to capture spatial patterns. A study describes how Vision Transformers perform better on large-scale image datasets than CNNs when trained with enough training data to achieve maximum performance. Additionally, hybrid architectures are investigated, which combine the advantages of CNNs and transformers strategies to improve generalization and training efficiency. It will help researchers learn about the latest deep-learning techniques and comparative analysis. It will also be helpful to know which type of Transformer is most suitable for accurately detecting the early stage of skin cancer classification and detection.