Advancing Skin Cancer Detection through Deep Learning and Fusion of Patient Metadata and Skin Lesion Images

There has been a significant rise in skin cancer incidence during the last three decades and the waiting time for skin lesion assessment in both the National Health Service ( NHS) and private sectors in the UK has increased significantly. Therefore, to reduce waiting time and to make a faster decision, there is a need to develop automated methods that can be used to classify whether a skin lesion is suspicious or non-suspicious during teledermatology triage. In this study, we propose an artificial intelligence ( AI) framework that uses patient metadata together with image data to classify skin lesions into suspicious or non-suspicious categories. To evaluate our proposed approach, we collected 79,246 skin lesion images along with their 22 meta-features such as lesion size, lesion colour, lesion shape, patient age, and gender from 19,295 patients who attended a network of private skin cancer diagnostic centres across the UK. We developed three separate models for skin lesion classification: 1) an AI model using only metadata that achieved 85.24% sensitivity and 61.12% specificity; 2) an AI model using only images that achieved 99.72% sensitivity and 63.22% specificity; and 3) a fused model based on both metadata and images that achieved 99.66% sensitivity and 74.45% specificity. The decisions of the developed AI models were then fused through a majority voting technique, which achieved a sensitivity of 99.50% and a specificity of 82.45%, significantly outperforming the state-of-the-art methods that rely solely on image data. Furthermore, we add a post-processing step to explain AI model decisions by implementing a soft-attention module that provides essential explainability and supports healthcare professionals in informed decision-making. The developed AI framework has great potential for the detection of suspicious skin lesions. With a reduction in patient referrals for possible biopsies, waiting times for skin cancer diagnosis and treatment will be shortened, resulting in improved outcomes.