Advancing skin cancer detection through deep learning and fusion of patient metadata and skin lesion images.

ABSTRACT: 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 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 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 patie

SUBMITTER: Islam S 

PROVIDER: S-EPMC12808132 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature