The classification of Covid-19 cases through the employment of transfer learning on X-ray images
Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of...
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/33664/1/The%20classification%20of%20covid19%20cases%20through%20the%20employment.pdf |
| _version_ | 1796995009746042880 |
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| author | Nur Ameerah, Hakimi Mohd Azhar, Mohd Razman Anwar P. P., Abdul Majeed |
| author_facet | Nur Ameerah, Hakimi Mohd Azhar, Mohd Razman Anwar P. P., Abdul Majeed |
| author_sort | Nur Ameerah, Hakimi |
| collection | UMP |
| description | Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of the people who are infected with the virus show no symptoms of the disease. Due to the number of confirmed cases rapidly rising around the world, it is crucial find another method to diagnose the disease at the beginnings stage in order to control the spreading of the virus. Another alternative test from the main screening method is by using chest radiology image based detection which are X-ray or CT scan images. The aim of this research is to classify the Covid-19 cases by using the image classification technique.The dataset consist of 2000 images of chest X-ray images and have two classes which are Covid and Non-Covid. Each of the class consists of 1000 images.This research compare the performance of the various Transfer Learning models (VGG-16, VGG-19, and Inception V3) in extracting the feature from X-ray image combined with machine learning model (SVM, kNN, and Random Forest) as a classifier. The experiment result showed the VGG-19, VGG-16, and Inception V3 coupled with optimized SVM pipelines are comparably efficient in classifying the cases as compared to other pipelines evaluated in this reaseach and could archieved 99% acuuracy on the test datasets. |
| first_indexed | 2024-03-06T12:56:00Z |
| format | Article |
| id | UMPir33664 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:56:00Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir336642022-04-11T02:28:43Z http://umpir.ump.edu.my/id/eprint/33664/ The classification of Covid-19 cases through the employment of transfer learning on X-ray images Nur Ameerah, Hakimi Mohd Azhar, Mohd Razman Anwar P. P., Abdul Majeed T Technology (General) TS Manufactures Covid-19 is a contagious disease that known to cause respirotary infection in humans. Almost 219 countries are effected by the outbreak of the latest coronavirus pandemic, exceed 100 millions of confirmed cases and about 2 million death recorded aound the world. This condition is alarming as some of the people who are infected with the virus show no symptoms of the disease. Due to the number of confirmed cases rapidly rising around the world, it is crucial find another method to diagnose the disease at the beginnings stage in order to control the spreading of the virus. Another alternative test from the main screening method is by using chest radiology image based detection which are X-ray or CT scan images. The aim of this research is to classify the Covid-19 cases by using the image classification technique.The dataset consist of 2000 images of chest X-ray images and have two classes which are Covid and Non-Covid. Each of the class consists of 1000 images.This research compare the performance of the various Transfer Learning models (VGG-16, VGG-19, and Inception V3) in extracting the feature from X-ray image combined with machine learning model (SVM, kNN, and Random Forest) as a classifier. The experiment result showed the VGG-19, VGG-16, and Inception V3 coupled with optimized SVM pipelines are comparably efficient in classifying the cases as compared to other pipelines evaluated in this reaseach and could archieved 99% acuuracy on the test datasets. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33664/1/The%20classification%20of%20covid19%20cases%20through%20the%20employment.pdf Nur Ameerah, Hakimi and Mohd Azhar, Mohd Razman and Anwar P. P., Abdul Majeed (2021) The classification of Covid-19 cases through the employment of transfer learning on X-ray images. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (1). pp. 44-51. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v3i1.7151 https://doi.org/10.15282/mekatronika.v3i1.7151 |
| spellingShingle | T Technology (General) TS Manufactures Nur Ameerah, Hakimi Mohd Azhar, Mohd Razman Anwar P. P., Abdul Majeed The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title | The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title_full | The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title_fullStr | The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title_full_unstemmed | The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title_short | The classification of Covid-19 cases through the employment of transfer learning on X-ray images |
| title_sort | classification of covid 19 cases through the employment of transfer learning on x ray images |
| topic | T Technology (General) TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/33664/1/The%20classification%20of%20covid19%20cases%20through%20the%20employment.pdf |
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