Skin Cancer Detection using Ensemble Learning and Grouping of Deep Models

Melanoma remains the most dangerous form of skin cancer which has a high mortality rate. When detect early, melanoma can be easily cured and millions of lives might be saved. The use of automatic detection models in clinical decision support can increase the ability to address this issue and improve survival rates. In this work, we proposed an automated pipeline for melanoma detection, which combines the predictions of deep convolutional neural network models through ensemble learning techniques. Furthermore, our automated pipeline includes various strategies such as image augmentation, upsampling, image cropping, digital hair removal and class weighting. Our pipeline was trained and tested using the image data acquired from the Society for Imaging Informatics in Medicine and the International Skin Imaging Collaboration SIIM-ISIC 2020. Our proposed pipeline has demonstrated a high performance compared to the other state-of-the-art pipelines for melanoma disease prediction with an accuracy of 97.77% and an AUC of 98.47%.