Deep Learning Ensemble Methods for Skin Lesion Analysis towards Melanoma Detection

Abstract

Skin cancer has a significant impact across the world. Melanoma is a malignant form of skin cancer. Skin lesion segmentation is an important step in computer-aided diagnosis (CAD) for automated diagnosis of melanoma. In this paper, we describe our research work and the submission to the International Skin Imaging Collaborations (ISIC) 2018 Challenge in Skin Lesion Analysis Towards Melanoma Detection. We propose Convolutional Neural Network (CNN) based ensemble methods for improving the existing performance of lesion segmentation. The proposed ensemble technique includes VGG19-UNet, DeeplabV3+ and other preprocessing methodologies. Extensive experiments are conducted on the ISIC 2018 challenge dataset to demonstrate the efficacy of the proposed model. For evaluation, we utilize the ISIC 2018 datasets that contains 2,594 dermoscopy images with their ground truth segmentation masks. We randomly divided the dataset into 80% for training and 20% for validation. Our proposed model provided an overall accuracy of 93.6%, average Jaccard Index of 0.815, and dice coefficient of 0.887 on the testing dataset.