COVID-19 Detection from X-ray Images Using a New CNN Approach
2022 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-1806962
ABSTRACT
Detection of COVID-19 has been a very active field of research with thousands of papers published after outbreaks of COVID-19 in the world. Computer-Aided Design (CAD) based studies have a significant role in the medical field thanks to rapidly developing technology. To help radiologists speed up the diagnostic process, CAD with convolutional neural networks (CNN) can be used as decision support mechanisms. Furthermore, CNN has the power to learn various image features automatically, and it may offer an effective way for COVID-19 detection. In this paper, we propose a CNN design for COVID-19 detection. We used a data set of X-ray images collected from two publicly available sources. This data set consists of 400 images of which 200 are COVID-19 and 200 are healthy. First, we preprocessed all data sets and then divided them by randomly allocating 70 % for training and 30 % for the test. We obtained the accuracy, specificity, and sensitivity rate of our model as 96.11%, 98.89 %, and 93.33 %, respectively. © 2022 IEEE. All rights reserved
Computer-Aided Design (CAD); Convolutional Neural Network (CNN); COVID-19; image classification; X-ray imaging; Computer aided design; Convolution; Convolutional neural networks; Decision support systems; Diagnosis; Active field; Computer-aided design; Convolutional neural network; Data set; Images classification; X-ray image
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2022
Year:
2022
Document Type:
Article
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