1. School of Optoelectronic Engineering, Guilin University of Electronic Technology, Guilin 541200, China
2. Harbin Engineering University, Harbin 150001, China
3. Key Lab of In-Fiber Integrated Optics, Ministry of Education, Harbin 150001, China
Abstract：The emergence of the novel coronavirus has caused significant losses in the global economy and public safety. The need for efficient detection and diagnosis has become urgent as the virus continues to inflict damage worldwide. Accurate and fast diagnosis of the novel coronavirus is critical for epidemic prevention and control. With the development of technology, deep learning has made many breakthroughs in detection and recognition, attracting widespread attention from researchers. However, deep learning requires a large amount of data for model training, and the collection of Raman spectra data for the novel coronavirus is limited by devices and environmental factors, making it difficult to obtain large amounts of data. The limited training data can hinder the training of deep learning models and limit their accuracy, resulting in poor performance in actual detection. To address this problem, this study introduces the CGAN adversarial network to automatically extract features from Raman spectra data and generate new spectra to expand the dataset for the novel coronavirus. These methods can effectively increase the training set's size and improve the model's accuracy. Using the enhanced Raman spectra dataset and deep neural networks, as well as traditional machine learning methods, including logistic regression, decision trees, random forests, support vector machines, and k-nearest neighbors algorithm, the diagnosis of COVID-19 is performed. The experimental results show that the deep neural network has a prediction accuracy of 98% for whether a patient is infected with the novel coronavirus, which is higher than traditional machine learning algorithms, demonstrating the superiority of deep learning models in Raman spectra detection of the novel coronavirus. We also compared the performance of the datasets before and after augmentation in different models, proving the effectiveness of data augmentation. Compared with traditional detection methods, this method is non-invasive, fast, and accurate, providing a new approach for biomedical detection of the novel coronavirus. The method proposed in this study can assist in rapidly and accurately detecting the novel coronavirus. It can be applied not only to the diagnosis of the novel coronavirus but also to the diagnosis of other diseases, having practical application value.
Key words：Novel coronavirus detection;Neural network;Raman spectra, Data augmentation;Qualitative classification
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