Harnessing RNN for Omicron Prediction via CT Scan Analysis

In recent trends we are again hearing the name SARS Cov-2, it underscored the urgent need for rapid and accurate diagnostic tools to combat the ongoing pandemic. This research introduces a fresh approach utilizing deep learning techniques to detect Omicron infection early, achieved through the analysis of CT scans.. Our research leverages a substantial dataset comprising 14,482 CT images obtained from diverse sources, including individuals with confirmed Omicron infection. A Convolutional Neural Network (CNN) is frequently utilized for image detection along with classification.. CNN are inspired by the structure of human brain. In CNNs, artificial neurons or nodes, similar to neurons in the brain, receive input, undergo processing, and produce output. However another challenge encountered when dealing with infection detection in terms of execution time and performance of prediction. Lack of inputs is the main cause for this, the proposed RNN technique using 14,482 CT scan images outperformed the existing in terms of accuracy and execution time. RNNs have the ability to examine temporal patterns found in CT images illustrating viral infections. Analyzing temporal patterns allows our model to grasp intricate patterns present in the dataset.