An Advanced Deep Learning Medical Image Recognition and Diagnosis of Respiratory System Viruses
4th International Conference on Computer and Applications, ICCA 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2283686
ABSTRACT
Respiratory infections are a confusing and time-consuming task that caused recently a pandemic that affected the whole world. One of the pandemics was COVID-19 that has exposed the vulnerability of medical services across the world, particularly in underdeveloped nations. There comes a strong demand for developing new computer-assisted diagnosis tools to present cost-effective and rapid screening in locations wherein enormous traditional testing is impossible. Medical imaging becomes critical for diagnosing disease, X-rays and computed tomography (CT) scan are employed in the deep network which will be helpful in diagnosing diseases. This paper proposes a scanning model based on using a Mel Frequency Cepstral Coefficients (MFCC) features extracted from a respiratory virus CT-Scan image and then filtered by applying Gabor filter (GF). The filtered image is passed to Convolutional Neural Network (CNN) for classifying the image for the presence of a respiratory virus such as Covid, Viral Pneumonia or being a healthy normal image. The proposed system achieved a validation accuracy of 100% with an overall accuracy of 99.44%. © 2022 IEEE.
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
4th International Conference on Computer and Applications, ICCA 2022
Year:
2022
Document Type:
Article
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