1D Convolutional neural networks and machine learning algorithms for spectral data classification with a case study for Covid-19

TL;DR

This study demonstrates that 1D convolutional neural networks outperform traditional machine learning algorithms in spectral data classification, including a case study on COVID-19 spectral samples, achieving high accuracy and potential for automated disease diagnosis.

Contribution

The paper introduces the application of 1D-CNN to spectral data classification and compares its performance with existing algorithms, highlighting its superior effectiveness especially for noisy and overlapping spectral signals.

Findings

1D-CNN achieved an average accuracy of 96.5%.

Spectral data classification improved with 1D-CNN over traditional methods.

Potential for automated COVID-19 diagnosis using spectral analysis.

Abstract

Machine and deep learning algorithms have increasingly been applied to solve problems in various areas of knowledge. Among these areas, Chemometrics has been benefited from the application of these algorithms in spectral data analysis. Commonly, algorithms such as Support Vector Machines and Partial Least Squares are applied to spectral datasets to perform classification and regression tasks. In this paper, we present a 1D convolutional neural networks (1D-CNN) to evaluate the effectiveness on spectral data obtained from spectroscopy. In most cases, the spectrum signals are noisy and present overlap among classes. Firstly, we perform extensive experiments including 1D-CNN compared to machine learning algorithms and standard algorithms used in Chemometrics on spectral data classification for the most known datasets available in the literature. Next, spectral samples of the SARS-COV2 virus, which causes the COVID-19, have recently been collected via spectroscopy was used as a case study. Experimental results indicate superior performance of 1D-CNN over machine learning algorithms and standard algorithms, obtaining an average accuracy of 96.5%, specificity of 98%, and sensitivity of 94%. The promissing obtained results indicate the feasibility to use 1D-CNN in automated systems to diagnose COVID-19 and other viral diseases in the future.