<![CDATA[Pneumonia is a lung infection that has a widespread impact on public health, particularly in areas with limited access to healthcare services. Chest X-ray imaging plays an important role in diagnosing this disease; however, low contrast quality often becomes an obstacle to automated classification using deep learning methods. This study aims to evaluate the effectiveness of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method in enhancing the visual quality of chest X-ray images and to analyze its impact on the performance of a Convolutional Neural Network (CNN) model in detecting pneumonia. CLAHE enhances the visibility of radiographic details through local contrast redistribution with a clip limit, allowing previously indistinct pathological structures to be more clearly recognized by the CNN. The dataset used consists of 2,623 X-ray images that are divided into two classes, namely Normal and Pneumonia. The training process was conducted under two scenarios, without and with the application of CLAHE. The evaluation results show that the CNN model without CLAHE achieved an accuracy of 96.18%, while the model with CLAHE improved to 99.69%. This improvement is significant as it reduced the classification error rate from approximately 3.8% in the model without CLAHE to only 0.3% in the model with CLAHE, while also increasing precision, recall, and f1-score across all classes. Therefore, combination of CLAHE and CNN can be applied as an effective approach for pneumonia detection that is accurate, consistent, and efficient, especially in environments with limited computational resources.]]>
Copyrights © 2025
