AUTOMATED LUNG DISEASE PREDICTION USING XCEPTION CNN FRAMEWORK
Dr.V.SASIKALA M.E.,Ph.D,
Assistant Professor(Sr.Gr.),
Department of Computer Science and Engineering,
University College of Engineering,Thirukkuvalai
AAKASH N, KAVYA R, KESAVARAM P
Department of Computer Science and Engineering
University College of Engineering,Thirukkuvalai
(A Constituent College Of Anna University::Chennai and Approved by AICTE,New Delhi)
Abstract— There are several lung illnesses in the world. This group of illnesses includes chronic obstructive pulmonary disease, pneumonia, asthma, TB, fibrosis, and others. The earliest possible diagnosis of lung illness is crucial. Many image processing and machine learning models have been created with this goal in mind. Since the release of the new Covid-19 for its accurate estimation, numerous forms of study have been started all over the world Because numerous people passed away from severe chest congestion brought on by the prior respiratory illness pneumonia, Covid19 is connected to that condition (pneumonic condition). It can be challenging for medical professionals to differentiate
between pneumonia and Covid-19 lung illnesses. Chest CTScan imaging is the most reliable 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. Use Multi-class CNN as well to identify various lung conditions like pneumonia and tuberculosis. The experimental results show that the proposed system improves disease prediction accuracy and also provides diagnosis details for the illnesses studied Index Terms— Deep learning, classifications, image processing, lung disorders, and CT scans.
