Skin cancer diagnosis using NIR spectroscopy data of skin lesions in
  vivo using machine learning algorithms
Flavio P. Loss Pedro H. da Cunha Matheus B. Rocha Madson Poltronieri Zanoni Leandro M. de Lima Isadora Tavares Nascimento Isabella Rezende Tania R. P. Canuto Luciana de Paula Vieira Renan Rossoni Maria C. S. Santos Patricia Lyra Frasson Wanderson Romão Paulo R. Filgueiras Renato A. Krohling
Skin lesions are classified in benign or malignant. Among the malignant,
melanoma is a very aggressive cancer and the major cause of deaths. So, early
diagnosis of skin cancer is very desired. In the last few years, there is a
growing interest in computer aided diagnostic (CAD) using most image and
clinical data of the lesion. These sources of information present limitations
due to their inability to provide information of the molecular structure of the
lesion. NIR spectroscopy may provide an alternative source of information to
automated CAD of skin lesions. The most commonly used techniques and
classification algorithms used in spectroscopy are Principal Component Analysis
(PCA), Partial Least Squares - Discriminant Analysis (PLS-DA), and Support
Vector Machines (SVM). Nonetheless, there is a growing interest in applying the
modern techniques of machine and deep learning (MDL) to spectroscopy. One of
the main limitations to apply MDL to spectroscopy is the lack of public
datasets. Since there is no public dataset of NIR spectral data to skin
lesions, as far as we know, an effort has been made and a new dataset named
NIR-SC-UFES, has been collected, annotated and analyzed generating the
gold-standard for classification of NIR spectral data to skin cancer. Next, the
machine learning algorithms XGBoost, CatBoost, LightGBM, 1D-convolutional
neural network (1D-CNN) were investigated to classify cancer and non-cancer
skin lesions. Experimental results indicate the best performance obtained by
LightGBM with pre-processing using standard normal variate (SNV), feature
extraction 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 the
first steps in CAD of skin lesions aiming the automated triage of patients with
skin lesions in vivo using NIR spectral data.