Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia
This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. While larger images ge...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2025-02-01
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| Series: | IJAIN (International Journal of Advances in Intelligent Informatics) |
| Subjects: | |
| Online Access: | https://ijain.org/index.php/IJAIN/article/view/1897 |
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| _version_ | 1850025420880084992 |
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| author | Kriengsak Yothapakdee Yosawaj Pugtao Sarawoot Charoenkhun Tanunchai Boonnuk Kreangsak Tamee |
| author_facet | Kriengsak Yothapakdee Yosawaj Pugtao Sarawoot Charoenkhun Tanunchai Boonnuk Kreangsak Tamee |
| author_sort | Kriengsak Yothapakdee |
| collection | DOAJ |
| description | This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. While larger images generally yield better results, there is a decline in performance beyond a certain size. Lowering the image resolution to 32x32 pixels significantly reduces performance to 83.00% likely due to the loss of diagnostic features. Furthermore, this study emphasizes the relationship between image size and processing time, empirically revealing that both increasing and decreasing image size beyond the optimal point results in increased training and testing time. The performance was noted with 299x299 pixel images completing the process in seconds. Our results indicate a balance between efficiency, as larger images slightly improved accuracy but slowed down speed, while smaller images negatively impacted precision and effectiveness. These findings assist in optimizing chest X-ray image sizes for pneumonia prediction models by weighing diagnostic accuracy against computational resources. |
| format | Article |
| id | doaj-art-43cd3e0813c24b8f863f41fdd5f65486 |
| institution | DOAJ |
| issn | 2442-6571 2548-3161 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Universitas Ahmad Dahlan |
| record_format | Article |
| series | IJAIN (International Journal of Advances in Intelligent Informatics) |
| spelling | doaj-art-43cd3e0813c24b8f863f41fdd5f654862025-08-20T03:00:50ZengUniversitas Ahmad DahlanIJAIN (International Journal of Advances in Intelligent Informatics)2442-65712548-31612025-02-01111253810.26555/ijain.v11i1.1897319Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting PneumoniaKriengsak Yothapakdee0Yosawaj Pugtao1Sarawoot Charoenkhun2Tanunchai Boonnuk3Kreangsak Tamee4Department of Computer Science, Faculty of Science and Technology, Loei Rajabhat UniversityInternal Medicine Department, Chumphae Hospital, Chum Phae District, Khon KaenHospital Director of Khok-Nong-Kae, Health Promoting Hospital of Wangsaphung DistrictDepartment of Public Health, Faculty of Science and Technology, Loei Rajabhat UniversityDepartment of Computer Science and Information Technology, Faculty of Science, Naresuan UniversityThis study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. The neural network algorithm achieved an accuracy rate of 87.00% across different image sizes. While larger images generally yield better results, there is a decline in performance beyond a certain size. Lowering the image resolution to 32x32 pixels significantly reduces performance to 83.00% likely due to the loss of diagnostic features. Furthermore, this study emphasizes the relationship between image size and processing time, empirically revealing that both increasing and decreasing image size beyond the optimal point results in increased training and testing time. The performance was noted with 299x299 pixel images completing the process in seconds. Our results indicate a balance between efficiency, as larger images slightly improved accuracy but slowed down speed, while smaller images negatively impacted precision and effectiveness. These findings assist in optimizing chest X-ray image sizes for pneumonia prediction models by weighing diagnostic accuracy against computational resources.https://ijain.org/index.php/IJAIN/article/view/1897chest x-raysuitable sizecovid-19machine learningpredictive model |
| spellingShingle | Kriengsak Yothapakdee Yosawaj Pugtao Sarawoot Charoenkhun Tanunchai Boonnuk Kreangsak Tamee Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia IJAIN (International Journal of Advances in Intelligent Informatics) chest x-ray suitable size covid-19 machine learning predictive model |
| title | Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia |
| title_full | Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia |
| title_fullStr | Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia |
| title_full_unstemmed | Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia |
| title_short | Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia |
| title_sort | finding a suitable chest x ray image size for the process of machine learning to build a model for predicting pneumonia |
| topic | chest x-ray suitable size covid-19 machine learning predictive model |
| url | https://ijain.org/index.php/IJAIN/article/view/1897 |
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