Classification of skin cancer images using two-level ensemble deep learning

1403 2 60 80 deep learning models for image classification including VGG, ResNet, and Inception are used in two levels.Specifically, we formed two ensembles; VGG ensemble which consists of VGG-16 and VGG-19 models and ResNet ensemble which consists of ResNet152, ResNet50, and Inception models.CatBoost algorithm is used in each level to combine the models on that ensemble.Finally, at the next level, two ensembles were combined using the CatBoost algorithm.The proposed ensemble model tries to improve the accuracy and consistency of the results by aggregating the deep models at its two levels.In order to show the utility of the proposed model, a subset of ISIC public dataset for skin cancer images is used for training and evaluation of models.The performance of the proposed ensemble model is compared with several deep neural networks and previous similar researches.Specifically, we compared the results achieved by the proposed model with those obtained by existing similar deep models and those used as building blocks of the proposed model.The results show that the proposed model performs better in classifying skin cancer images.The performance of the proposed model, both in each of the classes and in general, has been better than all independent deep learning models.It has also been shown that using VGG ensemble along with this proposed model by combining its results with the help of CatBoost and forming a two-level ensemble has improved its independent performance in each class.