For humankind, skin cancer is a troubling illness. Given the rapid growth rate of skin cancer, its high treatment cost and death rate, the need for early detection of skin cancer has been increased. Now, the world has evolved in a way where skin cancer detection is possible by image pre-processing and machine learning methods. One well known and well worked method is Convolutional Neural Network (CNN). After segmentation of dermoscopic images, the features of the affected skin cells are extracted using feature extraction technique. We propose a convolutional neural network model to detect cancerous state of a person’s skin and classify them as malignant (melanoma) and benign (non-malignant). The above model’s architecture contains various layers which helps in reading the dataset by computer. Accurate results are always expected in these cases. We are using manual approach instead of automatic approach to overcome possible errors.