Development of an AI-Based Skin Cancer Recognition Model and Its Application in Enabling Patients to Self-Triage Their Lesions with Smartphone Pictures

Abstract

Artificial intelligence (AI) based on convolutional neural networks (CNNs) has recently made great advances in dermatology with respect to the classification and malignancy prediction of skin diseases. In this article, we demonstrate how we have used a similar technique to build a mobile application to classify skin diseases captured by patients with their personal smartphone cameras. We used a CNN classifier to distinguish four subtypes of dermatological diseases the patients might have (“pigmentation changes and superficial infections”, “inflammatory diseases and eczemas”, “benign tumors, cysts, scars and callous”, and “suspected lesions”) and their severity in terms of morbidity and mortality risks, as well as the kind of medical consultation the patient should seek. The dataset used in this research was collected by the Department of Telemedicine of Albert Einstein Hospital in Sao Paulo and consisted of 146.277 skin images. In this paper, we show that our CNN models with an overall average classification accuracy of 79% and a sensibility of above 80% implemented in personal smartphones have the potential to lower the frequency of skin diseases and serve as an advanced tracking tool for a patient’s skin-lesion history.

Keywords

• artificial intelligence
• dermatology
• machine learning
• deep learning