Reducing False Prediction On COVID-19 Detection Using Deep Learning

被引：1

作者：

Bhowmik, Biswajit ^{[1
]}

Varna, Shrinidhi Anil ^{[1
]}

Kumar, Adarsh ^{[1
]}

Kumar, Rahul ^{[1
]}

机构：

[1] Natl Inst Technol Karnataka, Dept Comp Sci & Engn, Mangalore 575025, India

来源：

2021 IEEE INTERNATIONAL MIDWEST SYMPOSIUM ON CIRCUITS AND SYSTEMS (MWSCAS) | 2021年

关键词：

COVID-19; False Prediction; X-ray Images; Deep Neural Networks; Medical Imaging;

D O I：

10.1109/MWSCAS47672.2021.9531825

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a custom deep neural network-based scheme for coronavirus disease 2019 (COVID-19) detection. The proposed method takes X-ray images that use transfer learning techniques on pre-trained models. One objective of this work is to quickening the detection of the virus. Another goal is to reduce the number of falsely detected cases by a significant margin. The experimental setup demonstrates promising results on the selected dataset, which achieve up to 99.74%, 99.69%, 98.80% as classification, precision, and recall accuracy.

引用

页码：404 / 407

页数：4

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