This paper investigates the use of Vision Transformer models for the diagnosis of cancer-related skin lesions. The study focuses on six types of lesions: actinic keratosis (ACK), basal cell carcinoma (BCC), melanoma (MEL), nevus (NEV), squamous cell carcinoma (SCC), and seborrheic keratosis (SEK). The dataset employed included 2,298 smartphone photos of various resolutions, which provided a robust foundation for training and validation the model. To prepare the data, pre-processing techniques including rotation, zooming, and flipping were applied, increasing the minority classes and improving the diversity and robustness of the training set. The model was evaluated in terms of performance metrics, including an accuracy of 76%, a precision of 0.71, a recall of 0.73, and an F1 score of 0.72, illustrating the model’s robust ability to distinguish between different types of skin lesions. These findings show that incorporating deep learning into dermatological screening could improve the detection of worrisome lesions and ease early diagnosis, thereby contributing to the fight against skin cancer.