(Department of Computer Science, Faculty of Science and Technology, Loei Rajabhat University, Thailand)(2) Yosawaj Pugtao
(Internal Medicine Department, Chumphae Hospital, Chum Phae District, Khon Kaen, Thailand)(3) Sarawoot Charoenkhun
(Hospital Director of Khok-Nong-Kae, Health Promoting Hospital of Wangsaphung District, Thailand)(4) Tanunchai Boonnuk
(Department of Public Health, Faculty of Science and Technology, Loei Rajabhat University, Thailand)(5) Kreangsak Tamee
(Department of Computer Science and Information Technology, Faculty of Science, Naresuan University, Thailand)*corresponding author
AbstractThis 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.
KeywordsChest X-ray; Suitable size; Covid-19; Machine learning; Predictive model
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DOIhttps://doi.org/10.26555/ijain.v11i1.1897 |
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