Support Vector Machine Classification of NonmelanomaSkin Lesions Based on Fluorescence Lifetime Imaging Microscopy

Early diagnosis of malignant skin lesions is critical for prompt treatment and a clinical prognosis of skin cancers. However, it is difficult to precisely evaluate the development stage of nonmelanoma skin cancers because they are derived from the same tissues as a result of the uncontrolled growth of abnormal squamous keratinocytes in the epidermis layer of the skin. In the present study, we developed a linear-kernel support vector machine (LSVM) model to distinguish basal cell carcinoma (BCC) from actinic keratosis (AK) and Bowen’s disease (BD). The input parameters of the LSVM model consist of appropriate lifetime components and entropy values, which were extracted from two-photon fluorescence lifetime imaging of hematoxylin and eosin (H&E)-stained biopsy sections. Different features used as inputs for SVM training were compared and evaluated. In constructing the SVM models, features obtained from the lifetime (τ2) of the second component were found to be significantly more predictive than the average ...