COVID-19 IgG antibodies detection based on CNN-BiLSTM algorithm combined with fiber-optic dataset

Abstract

The urgent need for efficient and accurate automated screening tools for COVID-19 detection has led to research efforts exploring various approaches. In this study, we present pioneering research on COVID-19 detection using a hybrid model that combines convolutional neural networks (CNN) with a bi-directional long short-term memory (Bi-LSTM) network, in conjunction with fiber optic data for SARS-CoV-2 Immunoglobulin G (IgG) antibodies. Our research introduces a comprehensive data preprocessing pipeline and evaluates the performance of four different deep learning (DL) algorithms: CNN, CNN-RNN, BiLSTM, and CNN-BiLSTM, in classifying samples as positive or negative for the COVID-19 virus. Among these, the CNN-BiLSTM classifier demonstrated superior performance on the training datasets, achieving an accuracy of 89 %, a recall of 88 %, a precision of 90 %, an F1-score of 89 %, a specificity of 90 %, a geometric mean (G-mean) of 89 %, and a receiver operating characteristic (ROC) of 96 %. In addition, the achieved classification results were compared with those reported in the literature. The findings indicate that the proposed model has promising potential for classifying COVID-19 and could serve as a valuable tool for healthcare professionals. The use of IgG antibodies to detect the virus enhances the specificity and accuracy of the diagnostic tool.