Skin cancer is one of the most dangerous types of cancers that affect millions of people every year. The detection ofskin cancer in the early stages is an expensive and challenging process. In recent studies, machine learning-basedmethods help dermatologists in classifying medical images. This paper proposes a deep learning-based modelto detect and classify skin cancer using the concept of deep Convolution Neural Network (CNN). Initially, wecollected a dataset that includes four skin cancer image data before applying them in augmentation techniques toincrease the accumulated dataset size. Then, we designed a deep CNN model to train our dataset. On the test data,our model receives 95.98% accuracy that exceeds the two pre-train models, GoogleNet by 1.76% and MobileNetby 1.12%, respectively. The proposed deep CNN model also beats other contemporaneous models while beingcomputationally comparable.