QDCRNet: Quantum dilated convolutional recurrent network for virus detection using gene expression data

Viral Infections cause several common human illnesses, like the common cold, flu, mouth blisters, and chickenpox. However, numerous viruses, including rabies, hepatitis, Ebola, avian flu, and coronavirus cause significant health risks due to their high transmission rates. Infections are obligate intracellular parasites that depend on host cellular organisms, resources, and replication for their reproduction and spread. Timely and precise identification of these viruses is vital for appropriate treatment and preventing further spread. However common challenges such as, non-specific symptoms, variability in viral expression, and delayed testing often complicate timely diagnosis. To address this issue, a powerful module named Quantum Dilated Convolutional Recurrent Network (QDCRNet) has been developed for virus detection. Firstly, gene expression data is given into data transformation, and it is done by the Box-Cox transformation. Then, Feature Selection (FS) is performed using Gower distance and mutual information to select the virus-affected region. Finally, detection of the virus is done using QDCRNet, which is the integration of Quantum Dilated Convolutional Neural Network (QDCNN) and Deep Recurrent Neural Network (DRNN) model. The proposed QDCRNet has achieved a great performance with an accuracy of 90.80 %, sensitivity of 90.50 % and specificity of 90.40 %.