Prediction and analysis of Covid-19 using the Deep Learning Models

作     者：Indira, V. Geetha, R. Umarani, Srikanth Priyadarshini, D. Annal 

作者机构：Department of Computer Science and Engineering S.A. Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India 

出 版 物：《Research on Biomedical Engineering》 (Res. Biomed. Eng.)

年 卷 期：2024年第40卷第1期

页      面：183-197页

核心收录：

基　　金：Not applicable 

主　　题：COVID 19 

摘      要：Purpose: The difficulty of diagnosing several lung disorders, including atelectasis, cardiomegaly, lung cancer, and COVID-19, is a challenging problem and needs to be addressed. These conditions exhibit some symptoms and demand advanced medical imaging process, thorough clinical assessments, and innovative procedures for accurate diagnosis. The shortage of qualified radiologists further makes the problem more complex to deal with. COVID-19 in particular has resulted in a remarkable number of fatalities around the world. Children below the age of 5 and individuals over 65 are more likely to be affected by lung disorders. It is very hard to diagnose and manage COVID-19 absolutely, but it can be identified earlier by employing computer-aided diagnosis (CAD) technologies to make timely diagnosis. Currently, radiologists adopt technologies, which are driven by artificial intelligence. By using them, medical imaging data, such as chest X-rays and CT scans, can be investigated to identify patterns to diagnose the severity of the virus. This expedites the diagnostic process and enhances accuracy, facilitating more timely and precise medical interventions. The efficiency of artificial intelligence in processing large datasets can directly help healthcare professionals in making diagnosis quicker and more accurate. The objective of the work in this paper is to design and implement deep learning model classifiers, which will effectively categorize the patterns found in the X-rays and CT scans. Methods: Three techniques for categorization are exploited to propose an entirely new hybrid convolutional neural network (CNN) model in this context. MRI and CT image categorization in the first classification method employ Fully Connected (FC) layers. The weights are calculated and tuned for training the algorithm over multiple periods to deliver the maximum precision for classification. The most accurate MRI and CT image characteristics are studied, and deep learning model classifiers