High-Level Network-based Detection of Oral Cancer from ATR-FTIR Spectroscopy

This work investigates high-level classification techniques based on properties and measures of complex networks for the salivary detection of oral cancer from Attenuated Total Reflectance by Fourier Transform Infrared Spectroscopy (ATR-FTIR). Saliva biomarkers are alternative to surrogate other invasive samples in the early detection and monitoring of systemic diseases. Saliva also allows convenient and easy collection. ATR-FTIR is a sustainable, rapid and non-invasive platform able to contribute to the detection of several diseases. Traditional machine learning techniques have already been considered in the analysis of salivary ATR-FTIR data. However, such techniques are able to perform only low-level classification of the spectra data by considering physical features such as similarity, distance or distribution. On the other hand, high-level techniques are able to consider the semantic meaning of the input data by analyzing their structural and topological properties. In this paper, we investigate the hypothesis that the high-level classification of the ATR-FTIR spectra obtained via learning systems based on complex networks measures can achieve better predictive performance in comparison with low-level classification techniques widely adopted in the literature, such as linear discriminant analysis and support vector machines (SVM). Experiments conducted on real-world data confirmed our hypothesis. Our high-level classification techniques achieved 71% of accuracy and 81% of sensitivity in the detection of oral cancer after considering properties and structural patterns captured by the Clustering Coefficient network measure. Moreover, such results also outperformed those obtained by state-of-the-art classifiers like convolutional neural networks.