Predicting COVID-19 from Chest X-ray Images using a New Deep Learning Architecture
2022 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2022
; 2022-October, 2022.
Article
in English
| Scopus | ID: covidwho-2317865
ABSTRACT
The spread of coronavirus disease in late 2019 caused huge damage to human lives and forced a chaos in health care systems around the globe. Early diagnosis of this disease can help separate patients from healthy people. Therefore, precise COVID-19 detection is necessary to prevent the spread of this virus. Many artificial intelligent technologies for example deep learning models have been applied successfully for this task by employing chest X-ray images. In this paper, we propose to classify chest X-ray images using a new end-To-end convolutional neural network model. This new model consists of six convolutional blocks. Each block consists of one convolutional layer, one ReLU layer, and one max-pooling layer. The new model was applied on a challenging imbalanced COVID19 dataset of 5000 images, divided into two classes, COVID and Non-COVID. In experiments, the input image is first resized to 256×256×3 before being fed to the model. Two metrics were used to test our new model sensitivity and specificity. A sensitivity rate of 97% was achieved along with a specificity rate of 99.32%. These results are promising when compared to other deep learning models applied on the same dataset. © 2022 IEEE.
Convolutional Neural Networks; COVID-19; Image Classification; Convolution; Deep learning; Diagnosis; Learning systems; Viruses; Chest X-ray image; Convolutional neural network; Coronaviruses; Early diagnosis; Healthcare systems; Healthy people; Human lives; Images classification; Learning architectures; Learning models; Neural network models
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2022 IEEE Applied Imagery Pattern Recognition Workshop, AIPR 2022
Year:
2022
Document Type:
Article
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