<![CDATA[Pneumonia is a leading cause of morbidity and mortality, particularly in children, requiring rapid and accurate diagnosis. This study proposes a hybrid classification model that combines Gray Level Co-occurrence Matrix (GLCM) texture feature extraction with an Artificial Neural Network (ANN) to analyze chest X-ray images. The dataset consisted of 3,150 images, balanced using random undersampling. GLCM features were extracted across multiple distances and four orientations, generating 19 texture features per image. Seven experimental scenarios were conducted to evaluate ANN architectures with 2, 3, and 4 fully connected layers to identify the most effective configuration. The best-performing model achieved an accuracy of 91.50%, with precision, recall, and F1-score of 0.91, demonstrating consistent performance in distinguishing normal and pneumonia cases. Due to its relatively low computational complexity, this approach is suitable for low-resource healthcare settings. Future work will focus on expanding the dataset and validating the model with clinical data to enhance real-world applicability.]]>
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