According to the American Academy of Dermatology Association, identifying the types of skin cancer depends on the origin of a cell mutation resulting in the rapid growth of these abnormal cells in the epidermis. These mutations lead the skin cells to multiply rapidly and form malignant tumors. Skin cancer is ranked as the 17th most common form of cancer worldwide according to the World Cancer Research Fund. Skin cancer treatments cost the United States more than $8 billion (about $25 per person in the US) each year, making skin cancer the fifth most costly cancer for Medicare. Furthermore, skin cancer is an under recognized problem for diverse populations, including young women and minorities. Researchers have been exploring different technologies to detect skin care at its early stage to avoid high mortality rate and expensive medical treatment. This paper presents a novel ensembled deep learning model for the early detection of skin cancer. Our research is based on The HAM10000 dataset, a diverse collection of multi-sourced dermatoscopic images of common pigmented skin lesions which consists of 10015 dermatoscopic images. We have compared several deep learning neural network architectures and classifiers such as DNN, RNN, SVM and KNN in terms of accuracy rate and computation complexity and presented an ensembled deep learning model for early skin cancer detection. The main contribution of this paper is the productions of a comparative study of several skin cancer detection techniques using powerful computer vision techniques and deep learning models and a novel ensembled deep learning model for skin cancer detection.