Skin cancer is the most annoying type of cancer diagnosis according to its fast spread to various body areas, so it was necessary
to establish computer-assisted diagnostic support systems. State-of-the-art classifiers based on convolutional neural networks
(CNNs) are used to classify images of skin cancer. This paper tries to get the most accurate model to classify and detect skin
cancer types from seven different classes using deep learning techniques; ResNet-50, VGG-16, and the merged model of these
two techniques through the concatenate function. The performance of the proposed model was evaluated through a set of
experiments on the HAM10000 database. The proposed system has succeeded in achieving a recognition accuracy of up to
94.14%.