A Data Pre-processing Strategy Utilizing Adaptive Masking for the Classification of Pediatric Pneumonia Using VGG-16
DOI:
https://doi.org/10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5604Abstract
Pneumonia is still a leading cause of death in children, especially in areas with limited medical resources. This study aims to test several pre-processes to find the best set of pre-processes that can be applied to the children's chest X-ray dataset by applying adaptive masking, histogram equalization, CLAHE and Gaussian blur. Then, childhood pneumonia is classified using a CNN architecture, namely VGG-16. By applying these pre-processing methods, this study is divided into several scenarios. The highest accuracy was obtained from scenario 1, which used a combination of adaptive masking, histogram equalization and Gaussian blur, resulting in an accuracy of 94%. Scenario 2 uses histogram equalization and Gaussian blur with an accuracy of 92%. Then Scenario 3 uses a histogram equalization replacement for CLAHE with a combination of adaptive masking, CLAHE and Gaussian blur with 93% accuracy. Finally, scenario 4 uses a combination of CLAHE and Gaussian blur methods with 91% accuracy. In addition, this research also addresses the challenges posed by unbalanced data sets and the need for highly accurate detection tools.
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