Optimal Scree-CNN for Detecting NS1 Molecular Fingerprint from Salivary SERS Spectra.

Abstract

Dengue fever (DF) is a viral infection with possible fatal consequence. NS1 is a recent antigen based biomarker for dengue fever (DF), as an alternative to current serum and antibody based biomarkers. Convolutional Neural Network (CNN) has demonstrated impressive performance in machine learning problems. Our previous research has captured NS1 molecular fingerprint in saliva using Surface Enhanced Raman Spectroscopy (SERS) with great potential as an early, noninvasive detection method. SERS is an enhanced variant of Raman spectroscopy, with extremely high amplification that enables spectra of low concentration matter, such as NS1 in saliva, readable. The spectrum contains 1801 features per sample, at a total of 284 samples. Principal Component Analysis (PCA) transforms high dimensional correlated signal to a lower dimension uncorrelated principal components (PCs), at no sacrifice of the original signal content. This paper aims to unravel an optimal Scree-CNN model for classification of salivary NS1 SERS spectra. Performances of a total of 490 classifier models were examined and compared in terms of performance indicators [accuracy, sensitivity, specificity, precision, kappa] against a WHO recommended clinical standard test for DF, enzyme-linked immunosorbent assay (ELISA). Effects of CNN parameters on performances of the classifier models were also observed. Results showed that Scree-CNN classifier model with learning rate of 0.01, mini-batch size of 64 and validation frequency of 50, reported an across-the-board 100% for all performance indicators.