<![CDATA[X-ray imaging is a widely used technique for observing lung patients conditions. Compared to other radiographic methods, X-ray is more accessible, cost-effective, and commonly available in healthcare facilities. However, digital X-ray images often suffer from low quality, particularly in terms of image contrast, which complicates the process of identifying lung abnormalities accurately. In Embung Fatimah Hospital in Batam, X-ray imaging is routinely used to screen COVID-19 and healthy patients. To address the issue of poor image contrast, this study applies the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique, aiming to enhance image clarity and support more effective analysis. The research involved 20 lung X-ray images, consisting of 10 from COVID-19 and 10 from healthy patients, retrieved from the hospital’s radiology department system. The images underwent digital processing using Matlab software. The workflow included converting the images to grayscale before applying contrast enhancement with the CLAHE method, using three different distribution types: Uniform, Rayleigh, and Exponential. Following enhancement, Peak Signal to Noise Ratio and Mean Square Error metrics were calculated for each distribution type to evaluate image quality improvement. The result shown that all three CLAHE methods effectively enhanced the visual contrast of the lung images. The average MSE values for COVID-19 images were 26.27, 25.25, and 25.62, while for healthy images they were 28.27, 27.35, and 27.44. Meanwhile, the average PSNR values for COVID-19 images reached 155.63, 196.58, and 180.58, with healthy images scoring 98.27, 122.22, and 118.97. Overall, the process achieved an accuracy of 100%.]]>
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