Among the prevailing diseases on earth, cancer is one of the most dangerous one. To cure the cancer become difficult if the cancer cells persist in the body for a long time, so it is better to identify the cancer cells early and to start the treatment but some backward places do not have the accessibility for proper technology to identify the symptoms of cancer cells, especially for skin cancer which is increasing rapidly in the recent years due to increase in UV radiations that are reaching earth directly. Since experienced dermatologists are not mostly available in backward regions, Implementation of artificial intelligence can take place for detecting skin cancer in such remote places with non-professional dermatologists. Since the deep learning models are achieving human level accuracy in visualising the tasks and sometimes even with more accuracy than humans in the cases where things cannot be visualised by naked eye and where it is difficult for humans to classify. In this paper, A deep learning model is constructed which classifies the given image into any of the following seven categories. They are melanocytic nevus, dermatofibroma, melanoma, basal cell carcinoma, actinic keratosis, vascular lesion and benign keratosis. This model is trained on the HAM10000 dataset which is provided by an organization namely “The International Skin Imaging Collaboration (ISIC)”. This model can be trained and also be loaded into a mobile application which may have high resolution camera connected to it. So it can take the image of the patient skin where it is infected and feed the image as the input to our proposed deep learning model for evaluating the image as well as classifies the image to its particular class with high accuracy. © 2019, Advanced Scientific Research. All rights reserved.