Ensembling Learning Based Melanoma Classification Using Gradient Boosting Decision Trees

Abstract

Melanoma has been regarded as one of the fatal skin cancer diseases all around the world. Early detection on melanoma can be quite helpful in the clinical treatment, to prevent the deterioration of the deadly diseases. Handcrafted-feature extraction and shallow architecture-based classifier (such as k-nearest neighbors algorithm, random forest, support vector machine) worked as the basis of the previous attempts in detecting process. During the recent years, the new approach named deep convolutional neural network (CNN) was used for the detecting task. Although the persistent progress and efforts have been achieved, the classification methods desire to go a further step in pursuing further improvement on its performance. The goal of this paper is to improve the detection performance using an ensemble learning framework. Both the personal information (such as the age, gender information of the patients) and latest deep learning approaches are applied in this paper. The two approaches have provided the mutual complements for each other, which demonstrated enormous advantages for the ensemble learning framework in detecting task. We conducted extensive experiments that provide a large dataset for detecting melanoma, which illustrates that our ensemble learning can provide superior performance with high accuracy.