Machine learning-based classification of spatially resolved diffuse reflectance and autofluorescence spectra acquired on human skin for actinic keratoses and skin carcinoma diagnostics aid.

SIGNIFICANCE

The incidence of keratinocyte carcinomas (KCs) is increasing every year, making the task of developing new methods for KC early diagnosis of utmost medical and economical importance.

AIM

We aim to evaluate the KC diagnostic aid performance of an optical spectroscopy device associated with a machine-learning classification method.

APPROACH

We present the classification performance of autofluorescence and diffuse reflectance optical spectra obtained from 131 patients on four histological classes: basal cell carcinoma (BCC), squamous cell carcinoma (SCC), actinic keratosis (AK), and healthy (H) skin. Classification accuracies obtained by support vector machine, discriminant analysis, and multilayer perceptron in binary- and multi-class modes were compared to define the best classification pipeline.

RESULTS

The accuracy of binary classification tests was to discriminate BCC or SCC from H. For AK versus other classes, the classification achieved a 65% to 75% accuracy. In multiclass (three or four classes) classification modes, accuracy reached 57%. Fusion of decisions increased classification accuracies (up to 10 percentage point-increase), proving the interest of multimodal spectroscopy compared with a single modality.

CONCLUSIONS

Such levels of classification accuracy are promising as they are comparable to those obtained by general practitioners in KC screening.