Automated lung disease detection, classification and prediction using RNN framework

Abstract

Lung diseases are widespread throughout the globe. This group of illnesses includes chronic obstructive pulmonary disease, pneumonia, asthma, TB, fibrosis, and others. The earliest possible diagnosis of lung illness is crucial. Numerous image processing and artificial intelligence models have been created with this goal in mind. Several types of research have been initiated around the world since the arrival of the novel Covid-19 for its reliable estimation. The earlier respiratory disease pneumonia is linked to Covid-19 because several patients died as a result of severe chest congestion (pneumonic condition). Medical experts find it pulmonary illnesses caused by pneumonia and Covid-19 are difficult to differentiate. Chest CT-Scan imaging is the most accurate approach for predicting lung disease. Recently, a number of academics reported using AI-based methods to classify medical images using training data from CT scans. Deep learning is a very effective technique for understanding difficult cognitive difficulties, and more and more challenges are using and evaluating it. Recurrent neural network method, a deep learning system that can accurately detect COVID from CT-scan pictures, was employed in this study. Detect various lung illnesses like pneumonia and TB by using Multi-class RNN. The experimental findings demonstrate the suggested approach increases the precision of disease prediction and also gives information on the diagnoses of the illnesses under study.