EFFECTS OF CHEST X-RAY IMAGE SIZE ON MACHINE LEARNING PROCESSES AND THE EFFECTIVENESS OF CORONAVIRUS DISEASE 2019 PREDICTION MODELS
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Abstract
This research aimed to demonstrate how the differences in chest X-ray image sizes affect the execution time and efficiency of machine learning processes. The samples consisted of 299x299-pixel 15,153 chest X-ray images obtained from Kaggle.com. The experiment involved two approaches: reducing the image size to 20x20 and 30x30 pixels and increasing the image size to 800x800 and 1,024x1,024 pixels. The Random Forest algorithm was applied to build machine learning models for performance assessment. Two indicators, namely accuracy and execution time, were employed for the efficiency comparison. The findings revealed that the original 299x299 pixel chest X-ray images achieved an accuracy rate of 86.26% with an execution time of 9.17 minutes. For the X-ray images with the reduced sizes of 20x20 pixels and 30x30 pixels, the accuracy rates were 84.83% and 85.60%, and the execution times of 5.51 and 8.09 minutes. Conversely, enlarging the images to 800x800 and 1,024x1,024 pixels resulted in accuracy rates of 86.65% and 86.70%, with execution times of 28.56 and 31.06 minutes, respectively. This study proved that the execution time of machine learning processes and the effectiveness of image classification varied according to the size of chest X-ray images.
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