Nowadays skin cancer is a major problem human beings are facing. It requires detection at the early stage for the successful treatment. This study introduced a method for detecting skin cancer that combines image processing and deep learning. In order to recognize various types of skin cancer, the skin region or skin photos are fed into a convolutional neural network (CNN) model that is based on deep learning. Three two-dimensional convolutional layers, three pooling layers, two dropout layers, and one flattened and dense layer were employed in this CNN model. Here, we have worked with seven distinct forms of skin cancers and extending the number of epochs up to 100, we have achieved a very good accuracy. The augmented method is applied on the HAM10000 dataset to compute the performance and simulated in Jupyter notebook using Python.