Nonmelanoma skin cancer is one of the most common cancer types worldwide; it mainly includes Basal Cell Carcinoma (BCC) and Squamous Cell Carcinoma (SCC). In 2022, there were reported 1,234,602 cases and 69,481 deaths worldwide, ranking it as the fifth place of cancers with the most incidences in that year. Their treatment is similar, but, like the SCC type, has more incidence of metastasis than BCC, so that is necessary for radiotherapy or chemotherapy before any surgery of this type. This paper proposes an automated BCC and SCC classification technique using the Convolutional Neural Network (CNN) MobilNet. The dataset used is conformed for images from three different datasets and is applied to them through image processing to modify their dimensions without modifying the lesion’s shape. Additionally, various data augmentation techniques were applied to enrich the dataset. Finally, a deep CNN model with MobilNet architecture was designed to train the dataset. The model achieved 86.1% accuracy, 83.3% precision, 88.2% sensitivity and 84.2% specificity on the test data.