期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Smart COVID-3D-SCNN: A Novel Method to Classify X-ray Images of COVID-19
1
作者 Ahed Abugabah Atif Mehmood +1 位作者 ahmad ali a.l zubi Louis Sanzogni 《Computer Systems Science & Engineering》 SCIE EI 2022年第6期997-1008,共12页
The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant co... The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant contribution to medical image analysis.In classification detection,one or more images(detection)are usually used as input,and diagnostic variables(such as whether there is a disease)are used as output.The novel coronavirus has spread across the world,infecting millions of people.Early-stage detection of critical cases of COVID-19 is essential.X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early.For extracting the discriminative features through these modalities,deep convolutional neural networks(CNNs)are used.A siamese convolutional neural network model(COVID-3D-SCNN)is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans.To extract the useful features,we used three consecutive models working in parallel in the proposed approach.We acquired 575 COVID-19,1200 non-COVID,and 1400 pneumonia images,which are publicly available.In our framework,augmentation is used to enlarge the dataset.The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%,specificity 95.55%,and sensitivity 96.62%over(COVID-19 vs.non-COVID19 vs.Pneumonia). 展开更多
关键词 Convolutional neural network CLASSIFICATION X-RAY deep learning
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部