There are many types of skin cancer, the most harmful among them is melanoma. Skin cancer occurs through the abnormal growth of the body cells, which may be caused by continuous exposure to ultraviolet rays resulting from the sun. Early diagnosis of skin cancer is essential as it can reduce the burden, make the treatment more effective and save the patient life. In this work, therefore, we develop an enhanced ensemble approach to improve the classification accuracy of eight types of skin cancer. Three pretrained Convolutional Neural Network (CNN) models, namely ResNet18, DensNet121, and Inception v4, are used as a base for this ensemble. Firstly, we fine-tune each pre-trained CNN model separately on an augmented dataset. Afterwards, the prediction probabilities obtained from all the base learners are combined as a new feature vector for each case. These features are used to build another classifier that employs the outputs of all the networks used in the previous stage. Thus, this ensemble learns how to weigh the output of each base learner without any user interaction. Support vector machine (SVM), and random forest (RF) classifiers are utilized in this stage and compared to the average ensemble which gives the same importance to all base learners.The pre-trained CNN models were fine-tuned and evaluated using 17731 and 3800 images from different types of skin cancer, respectively. The ResNet18, DensNet121, and InceptionV4 achieved individual accuracy of 79.5%, 81.2%, and 82.6%, respectively. The proposed ensemble method using SVM, and RF classifiers gives the best accuracy result with 85% for the SVM classifier and 86.2% for the Random Forest classifier. These results show that the proposed ensemble method using SVM, and RF classifiers is superior to the single classifiers of ResNet18, DensNet121 and InceptionV4 as well as the commonly used average ensemble which achieved an accuracy of 83.2%.