This model detects 6 different skin cancers at a 95.37% accuracy

Every year, more people in the U.S are diagnosed with skin cancer than all other cancers combined. Early detection of these cancers can tremendously increase the 5-year survival rate. Current methods involve self skin examination (SSE), leading to professional screening from dermatologists. However, SSE can miss important lesions, which can cause development of cancer. To help aid in SSE and professional screening, efforts have been made towards the use of AI through Convolutional Neural Networks (CNN). However, these networks struggle to achieve high enough accuracy, preventing them from adequate use. This research proposes a Vision Transformer (ViT) model to increase the accuracy of skin cancer detection and its applicability. Through the ViT model’s multi-head attention layer, the model serves to be more suitable for skin cancer classification than the CNN's Conv2d layer.