Exploring Machine Learning Pipelines for Raman Spectral Classification of COVID-19 Samples

Raman Spectroscopy can analyze and identify the chemical compositions of samples. This study aims to develop a computational method based on machine learning algorithms to classify Raman spectra of serum samples from COVID-19 infected and non-infected human subjects. The method can potentially serve as a tool for rapid and accurate classification of COVID-19 versus non-COVID-19 patients and toward a direction for biomarker discoveries in research. Different machine learning classifiers were compared using pipelines with different dimensionality reduction and scaler techniques. The performance of each pipeline was investigated by varying the associate parameters. Assessment of dimensionality reduction application suggests that the pipelines generally performed better when the number of components does not exceed 50. The LightGBM model with ICA and MMScaler applied, yielded the highest test accuracy of 98.38% for pipelines with dimensionality reduction while the SVM model with MMScaler applied yielded the highest test accuracy of 96.77% for pipelines without dimensionality reduction. This study shows the effectiveness of Raman spectroscopy to classify COVID-19-induced characteristics in serum samples. © 2022 IEEE.

Mobile-based Application for COVID-19 Detection from Lung X-Ray Scans with Artificial Neural Networks (ANN)

In early 2020, the World Health Organization (WHO) identified a novel coronavirus referred to as SARS-CoV-2, which is associated with the now commonly known COVID-19 disease. COVID-19 was shortly later characterized as a pandemic. All countries around the globe have been severely affected and the disease has accumulated a total of over 200 million cases and more than five million deaths in the past two years. Symptoms associated with COVID-19 vary greatly in severity. Some infected with COVID-19 are asymptomatic, while others experience critical disease with life-threatening complications. In this paper, a mobile-based application has been created to help classify Covid-19 and non-Covid-19 lung when given an image of a Chest X-Ray (CXR). A variety of different artificial neural networks (ANN) including our baseline model, InceptionV3, MobileNetV2, MobileNetV3, VGG16, and VGG19 were tested to see which would provide the optimal results. It is concluded that MobileNetV3 gives the best test accuracy of 95.49% and is considered a lightweight model suitable for a mobile-based application. © 2022 IEEE.