Designing a Novel Learning Approach for Infectious Lung Disease Prediction using Bi-Directional Recurrent Neural Network (BRNN)

Abstract

The principal goal of this research is to use regular imaging to diagnose and classify several lung disorders, including pneumonia, COVID-19 and tuberculosis.A novel Bi-directional Recurrent Neural Network (BRNN) model is implemented in this work to categorize the disease according to the input samples given. With the help of the feature extractor, prediction model, and classification model, the suggested model performs feature analysis. The E2E model that has been suggested carries out knowledge acquisition using an efficient computational and memory approach. The benchmark dataset, made accessible online, was used to test the proposed model. When networks are trained to extract characteristics, the developments in deep learning techniques yield encouraging results and offer higher potential efficiency than biomedical applications. The main goal is to evaluate the significance of current methods and validate the BRNN model to address the concerns that are currently present. The suggested model offers better efficiency and performance for better detection. The results reveal that the model with a recurrent network works better than the current methods, with an accuracy of 71% for all diseases, whereas the traditional network models perform worse. The suggested model has less computational overhead and fewer training parameters. When compared to alternative methods, the model offers a superior trade-off.