A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
This provides a tool to assist doctors in early skin cancer screening, but it is incremental as it builds on existing deep learning methods.
The authors tackled skin cancer detection by developing a smartphone application using a CNN trained on clinical images and patient demographics, achieving a balanced accuracy of 85% and recall of 96%.
Over the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase patient prognosis. Recently, deep neural networks (DNNs) have become viable to deal with skin cancer detection. In this work, we present a smartphone-based application to assist on skin cancer detection. This application is based on a Convolutional Neural Network(CNN) trained on clinical images and patients demographics, both collected from smartphones. Also, as skin cancer datasets are imbalanced, we present an approach, based on the mutation operator of Differential Evolution (DE) algorithm, to balance data. In this sense, beyond provides a flexible tool to assist doctors on skin cancer screening phase, the method obtains promising results with a balanced accuracy of 85% and a recall of 96%.