Novel Quanvolutional Neural Network-Based COVID-19 Diagnosis From Raman Spectroscopic Signals of Human Serum - Shivanya Shomir Dutta; Sahil Sandeep; S. Sridevi; Rajiv Mistry; Jonathan Ortega; Gorkem Kar (IEEE Access)
COVID-19 has proven to be one of the most devastating pandemics of the 21st century, causing widespread mortality and overwhelming healthcare systems globally. Early and accurate diagnosis is crucial to managing and controlling the spread of this virus. Although the Polymerase Chain Reaction (PCR) technique is effective for identifying the genetic material of the pathogens causing SARS-CoV-2 COVID-19, its longer processing time has led to the emergence of Raman spectroscopy as a promising alternative, a non-destructive method that provides detailed molecular insights for detecting COVID-19 through human serum samples. The primary goal of this research is to accurately diagnose COVID-19 by analyzing Raman spectroscopic signals of serum samples with a novel quantum circuit-based Quanvolutional Neural Network (QNN). In this research, although the signals are initially in one dimension (1D), we applied wavelet transforms to convert Raman spectroscopic signals into 2D scaleogram images to capture intricate spectral patterns for efficient classification. We compared the performance of our proposed QNN with existing QNN variant and classical convolutional neural network (CNN) models such as Vanilla CNN, MobileNet CNN, Xception CNN, and VGG-16 CNN. We were surprised to achieve outstanding performance across all seven evaluation metrics namely Jaccard index(JI) of 97.73%, accuracy of 98.92%, F1-score of 98.85%, recall of 97.73%, Matthews correlation coefficient (MCC) of 97.86%, precision of 100% and Area Under the Curve (AUC) score of 98.86%. The results demonstrate that proposed novel QNN outperforms classical models without overfitting. Our findings highlight the potential of QNN in revolutionizing diagnostic tools for critical diseases like COVID-19.