Deep reinforced neural network model for cyto-spectroscopic analysis of epigenetic markers for automated oral cancer risk prediction

Abstract

Understanding epigenetic changes can provide vital information for early stage oral cancer diagnosis. Vibrational spectroscopy methods like Raman spectroscopy (RS) and Fourier Transform Infrared Spectroscopy (FTIR) can provide several advantages over conventional molecular biology methods, by incorporating information from fingerprint regions. Moreover, application of advanced spectral analysis tools like deep learning (DL) techniques can be efficiently applied for analyzing the large spectral dataset and extracting the vital features. Epigenetic changes are identifies in oral epithelial cells of healthy individuals, oral leucoplakia and squamous cell carcinoma patients through analysis of Raman (400-1800 ​cm−1) and FTIR data (700-2000 ​cm−1). Deep reinforced neural network (DRNN) model is employed to classify the epigenetic changes identified from the Raman and FTIR spectra. Feature extraction layer of DL model uses peak detection layer and reinforced learning layer to identify significant epigenetic features. Classification layer is made up of N numbers of back propagated Artificial Neural Network (ANN) layers. DL model developed is fully automated and overcome the wave shift problem of spectroscopic data. Testing accuracy of the proposed DRNN model is 83.33%. Class wise accuracies for NRML, OLPK and OSCC are 83.3%, 87% and 95.24%, respectively. Proposed DRNN model attains an overall ROC of 0.88 Present study employs combination of two complementary vibrational spectroscopy methods for oral cancer detection and chemometric analysis of the spectral features with DRNN mode. Identification of the epigenetic changes and utilization of the knowledge in cancer prediction will enable the proposed study to develop smart point-of-care diagnostic system.