2019年冠状病毒病(COVID-19)
人工智能
计算机科学
卷积神经网络
肺炎
深度学习
二元分类
病死率
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
机器学习
医学
流行病学
病理
支持向量机
内科学
传染病(医学专业)
疾病
作者
Priyansh Kedia,Anjum,Rahul Katarya
标识
DOI:10.1016/j.asoc.2021.107184
摘要
The ongoing fight with Novel Corona Virus, getting quick treatment, and rapid diagnosis reports have become an act of high priority. With millions getting infected daily and a fatality rate of 2%, we made it our motive to contribute a little to solve this real-world problem by accomplishing a significant and substantial method for diagnosing COVID-19 patients.The Exponential growth of COVID-19 cases worldwide has severely affected the health care system of highly populated countries due to proportionally a smaller number of medical practitioners, testing kits, and other resources, thus becoming essential to identify the infected people. Catering to the above problems, the purpose of this paper is to formulate an accurate, efficient, and time-saving method for detecting positive corona patients.In this paper, an Ensemble Deep Convolution Neural Network model "CoVNet-19" is being proposed that can unveil important diagnostic characteristics to find COVID-19 infected patients using X-ray images chest and help radiologists and medical experts to fight this pandemic.The experimental results clearly show that the overall classification accuracy obtained with the proposed approach for three-class classification among COVID-19, Pneumonia, and Normal is 98.28%, along with an average precision and Recall of 98.33% and 98.33%, respectively. Besides this, for binary classification between Non-COVID and COVID Chest X-ray images, an overall accuracy of 99.71% was obtained.Having a high diagnostic accuracy, our proposed ensemble Deep Learning classification model can be a productive and substantial contribution to detecting COVID-19 infected patients.
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