<![CDATA[Chest X-ray imaging is widely used to support the diagnosis of lung diseases, yet many automated similarity techniques still rely on RGB formats, which differ from the grayscale images commonly used in clinical systems. This discrepancy raises the question of whether color information is necessary for effective similarity assessment. This study aims to evaluate the performance of RGB and grayscale pixel-based similarity methods for lung X-ray analysis and determine whether grayscale images can provide comparable similarity performance with lower computational demands. A total of 300 chest X-ray images representing normal, pneumonia, and COVID-19 categories were processed in both formats. Pixel-level similarity was calculated across 30,000 image pairings, followed by statistical testing to assess differences between methods. The results show that grayscale similarity scores closely match those of RGB, with variations generally below 0.3%. A meaningful difference was observed only in the comparison between normal and COVID-19 images, indicating that RGB may capture subtle visual variations not present in grayscale. Overall, this study demonstrates that grayscale pixel-based similarity analysis provides a reliable and computationally efficient approach, contributing to the development of lightweight medical image retrieval and clinical decision support systems in the field of health informatics.]]>
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