The first melanoma tumor has affected millions of people across the globe and taken many human lives. It can be diagnosed in its early stage, therefore it becomes very important to detect it before it becomes lethal. The melanoma skin cancer can be detected from the images of tumor by applying various techniques of deep learning. Medical science has progressed to a large extent in recent times. Its progress can be catalyzed further with the help of technology such as artificial intelligence or deep learning. In the first stage of our study, we used CNN (convolutional neural network) and transfer learning for differentiating between normal and melanoma tumors. In the next stage, features of the image were extracted from different pretrained models and then these features were passed through global average pooling layer and a classifier was put on top of it. In the first stage, that is, end-to-end learning, MobileNet architecture achieved the highest F1 score of 0.8014. In feature extraction technique, the model in which features were extracted from MobileNet architecture and XGBoost was used as a classifier achieved the highest F1 score of 0.818.