Scaling-Up the Impact of Teledermoscopy on the Early Detection of Skin Melanoma using Convolutional Neural Networks with Mobile Apps

Advances in the cloud technology for secured distributed data storage, modern techniques for machine learning (ML), and access to large populations through mobile apps provide a unique opportunity for the healthcare industry professionals in the areas of early screening and medical diagnostics for certain diseases. This research study demonstrates the potential of ML using convolutional neural networks (CNN) for medical diagnostics of skin melanoma. Specifically, a comparison is presented between a shallow CNN (3-layers) with Resnet50 (50-layers) to classify open datasets of skin melanoma images as malignant or benign. Various ML performance metrics such as accuracy, recall, precision and receiver operating characteristic (ROC) are presented to recommend a deep learning model for the mobile app. Also, a novel framework is proposed for the scalability and adoption of ML-based medical diagnostics by large masses as a mobile app running on data-secure cloud platform. Using the open datasets, it is shown that skin cancer can be accurately diagnosed with a mobile phone app while maintaining patient privacy and data security.