A Multi-model Ensemble Method Using CNN and Maximum Correntropy Criterion for Basal Cell Carcinoma and Seborrheic Keratoses Classification

Basal cell carcinoma is very similar to the clinical traits of seborrheic keratosis, which is still a difficult problem in medical image analysis. To accurately classify it, this paper proposes a multi-model ensemble method based on the maximum correntropy criterion (MCC) and convolutional neural network (CNN). First of all, it is well known that the CNN single models like ResNet, Xception, DensNet, etc. have a good effect on the classification, but the accuracy is still limited, so the multi-model ensemble method is presented to improve the accuracy. Secondly, the traditional multi-model ensemble methods, such as voting and linear regression, can improve the accuracy of the model, but it means that the weight computation of each model does not consider the noise, and could not obtain good results. Therefore, we propose the MCC for the model ensemble, which overcomes the noise in the data and effectively improves the classification accuracy. Finally, our proposed multi-model ensemble algorithm based on the MCC achieved an accuracy of 97.07% in the basal cell carcinoma and seborrheic keratosis classification experiments, surpassing the CNN single model and traditional multi-model ensemble method.