Automated multi-class skin cancer classification through concatenated deep learning models

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