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Skin lesions are classified in benign or malignant. Among the malignant,melanoma is a very aggressive cancer and the major cause of deaths. So, earlydiagnosis of skin cancer is very desired. In the last few years, there is agrowing interest in computer aided diagnostic (CAD) using most image andclinical data of the lesion. These sources of information present limitationsdue to their inability to provide information of the molecular structure of thelesion. NIR spectroscopy may provide an alternative source of information toautomated CAD of skin lesions. The most commonly used techniques andclassification algorithms used in spectroscopy are Principal Component Analysis(PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and SupportVector Machines (SVM). Nonetheless, there is a growing interest in applying themodern techniques of machine and deep learning (MDL) to spectroscopy. One ofthe main limitations to apply MDL to spectroscopy is the lack of publicdatasets. Since there is no public dataset of NIR spectral data to skinlesions, as far as we know, an effort has been made and a new dataset namedNIR-SC-UFES, has been collected, annotated and analyzed generating thegold-standard for classification of NIR spectral data to skin cancer. Next, themachine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutionalneural network (1D-CNN) were investigated to classify cancer and non-cancerskin lesions. Experimental results indicate the best performance obtained byLightGBM with pre-processing using standard normal variate (SNV), featureextraction providing values of 0.839 for balanced accuracy, 0.851 for recall,0.852 for precision, and 0.850 for F-score. The obtained results indicate thefirst steps in CAD of skin lesions aiming the automated triage of patients withskin lesions in vivo using NIR spectral data.

Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms