A new method for detecting COVID-19 combining deep learning with improved histogram equalization and median filter
2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021
; : 119-125, 2021.
Article
in English
| Scopus | ID: covidwho-1948769
ABSTRACT
The new coronavirus (COVID-2019) epidemic outbreak has devastating impacts on people's daily lives and public healthcare systems. The chest X-ray image is an effective tool for diagnosing new coronavirus diseases. This paper proposes a new method to identify the new coronavirus from chest X-ray images to assist radiologists in fast and accurate image reading. We first enhance the contrast of X-ray images by using adaptive histogram equalization and eliminating image noise by using a median filter. Then, the X-ray image is fed to a sophisticated deep neural network (FAC-DPN-SENet) proposed by us to train a classifier, which is used to classify an X-ray image as usual or COVID-2019 or other pneumonia. Applying our method to a standard dataset, we achieve a classification accuracy of 93%, which is significantly better performance than several other state-of-the-art models, such as ResNet and DenseNet. This shows that the proposed method can be used as an effective tool to detect COVID-2019. © 2021 IEEE.
contrast-limited adaptive histogram; deep learning; median filter; Classification (of information); Coronavirus; Deep neural networks; Diagnosis; Equalizers; Graphic methods; Image enhancement; Adaptive histograms; Chest X-ray image; Coronaviruses; Effective tool; Equalization filters; Improved histogram equalization; Median-Filter; X-ray image; Median filters
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2nd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2021
Year:
2021
Document Type:
Article
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