CS-based lung covid-affected x-ray image disorders classification using convolutional neural network
Lung diseases, such as pneumonia, significantly affect the respiratory system, especially the lungs. This condition causes various degrees of lung damage in patients of all age groups, including the elderly, adults, and children. Even after treatment and recovery, diagnosing lung damage remains impo...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Bright Publisher
2024
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/43963/1/CS-based%20lung%20covid-affected%20x-ray%20image%20disorders%20classification.pdf |
| _version_ | 1825816156156461056 |
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| author | Triasari, Biyantika Emili Budiman, Gelar Siti Sarah, Maidin Mohd Izham, Mohd Jaya Sun Hariyani, Yuli Sun Irawati, Indrarini Dyah Zhao, Zhong |
| author_facet | Triasari, Biyantika Emili Budiman, Gelar Siti Sarah, Maidin Mohd Izham, Mohd Jaya Sun Hariyani, Yuli Sun Irawati, Indrarini Dyah Zhao, Zhong |
| author_sort | Triasari, Biyantika Emili |
| collection | UMP |
| description | Lung diseases, such as pneumonia, significantly affect the respiratory system, especially the lungs. This condition causes various degrees of lung damage in patients of all age groups, including the elderly, adults, and children. Even after treatment and recovery, diagnosing lung damage remains important, which can be done using rapid tests, clinical evaluations, CT scans, or X-rays. This study focuses on the classification of X-ray images of lungs affected by pneumonia and normal lungs, using the Convolutional Neural Network method based on Compressive Sensing (CS) simulated using MatLab. The purpose of the study is to determine the performance by calculating the confusion matrix value. The number of datasets used for normal lungs and those affected by pneumonia is 300 X-ray images from several different sources, with 60% training data, 30% validation, and 10% testing. The addition of the compression process causes a decrease in image quality, expressed in PSNR, as well as a decrease in classification parameters such as accuracy. Compared with previous research, the system without compression produces the highest accuracy. The results of the study can help classify lungs affected by pneumonia. |
| first_indexed | 2025-03-06T04:06:14Z |
| format | Article |
| id | UMPir43963 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2025-03-06T04:06:14Z |
| publishDate | 2024 |
| publisher | Bright Publisher |
| record_format | dspace |
| spelling | UMPir439632025-03-03T01:33:09Z http://umpir.ump.edu.my/id/eprint/43963/ CS-based lung covid-affected x-ray image disorders classification using convolutional neural network Triasari, Biyantika Emili Budiman, Gelar Siti Sarah, Maidin Mohd Izham, Mohd Jaya Sun Hariyani, Yuli Sun Irawati, Indrarini Dyah Zhao, Zhong QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Lung diseases, such as pneumonia, significantly affect the respiratory system, especially the lungs. This condition causes various degrees of lung damage in patients of all age groups, including the elderly, adults, and children. Even after treatment and recovery, diagnosing lung damage remains important, which can be done using rapid tests, clinical evaluations, CT scans, or X-rays. This study focuses on the classification of X-ray images of lungs affected by pneumonia and normal lungs, using the Convolutional Neural Network method based on Compressive Sensing (CS) simulated using MatLab. The purpose of the study is to determine the performance by calculating the confusion matrix value. The number of datasets used for normal lungs and those affected by pneumonia is 300 X-ray images from several different sources, with 60% training data, 30% validation, and 10% testing. The addition of the compression process causes a decrease in image quality, expressed in PSNR, as well as a decrease in classification parameters such as accuracy. Compared with previous research, the system without compression produces the highest accuracy. The results of the study can help classify lungs affected by pneumonia. Bright Publisher 2024-12 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/43963/1/CS-based%20lung%20covid-affected%20x-ray%20image%20disorders%20classification.pdf Triasari, Biyantika Emili and Budiman, Gelar and Siti Sarah, Maidin and Mohd Izham, Mohd Jaya and Sun Hariyani, Yuli Sun and Irawati, Indrarini Dyah and Zhao, Zhong (2024) CS-based lung covid-affected x-ray image disorders classification using convolutional neural network. Journal of Applied Data Sciences, 5 (4). pp. 1939-1948. ISSN 2723-6471. (Published) https://doi.org/10.47738/jads.v5i4.371 https://doi.org/10.47738/jads.v5i4.371 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Triasari, Biyantika Emili Budiman, Gelar Siti Sarah, Maidin Mohd Izham, Mohd Jaya Sun Hariyani, Yuli Sun Irawati, Indrarini Dyah Zhao, Zhong CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title | CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title_full | CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title_fullStr | CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title_full_unstemmed | CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title_short | CS-based lung covid-affected x-ray image disorders classification using convolutional neural network |
| title_sort | cs based lung covid affected x ray image disorders classification using convolutional neural network |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/43963/1/CS-based%20lung%20covid-affected%20x-ray%20image%20disorders%20classification.pdf |
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