  Skin cancer is one of the most common forms of cancer and its incidence is
projected to rise over the next decade. Artificial intelligence is a viable
solution to the issue of providing quality care to patients in areas lacking
access to trained dermatologists. Considerable progress has been made in the
use of automated applications for accurate classification of skin lesions from
digital images. In this manuscript, we discuss the design and implementation of
a deep learning algorithm for classification of dermoscopy images from the
HAM10000 Dataset. We trained a convolutional neural network based on the
ResNet50 architecture to accurately classify dermoscopy images of skin lesions
into one of seven disease categories. Using our custom model, we obtained a
balanced accuracy of 91% on the validation dataset.
