Deep ensemble learning using a demographic machine learning risk stratifier for binary classification of skin lesions using dermatoscopic images

Background: Skin lesion classification through dermatoscopic images is the most common method for non-invasive diagnostics of dermatologic conditions. Feature extraction through deep learning (DL) based convolutional neural networks (CNNs) provides insight into differential attributes of skin lesions that may pertain to its malignancy. In this study, we sought to improve the performance of standard CNN architectures in skin lesion classification by providing a machine learning (ML)-derived risk score from patient demographic data.