<![CDATA[Pneumonia classification from chest X-ray (CXR) images remains challenging because radiographic appearance varies in contrast, intensity, anatomical structure, and disease manifestation, while class imbalance may affect automated classification. This study proposes a transfer learning framework based on the Xception architecture for binary pneumonia classification from CXR images. The proposed framework integrates systematic image preprocessing, including image resizing, Contrast Limited Adaptive Histogram Equalization (CLAHE), Xception-specific normalization, training-only data augmentation, and class weighting. The dataset consisted of 5,856 CXR images and was stratified into 70% training, 20% validation, and 10% testing subsets. Xception was trained using a two-phase strategy, comprising 20 epochs with the backbone frozen and 30 epochs of fine-tuning with the last 30 layers selected while Batch Normalization layers remained frozen. Evaluation on 586 testing images achieved 95.56% accuracy, 98.79% Pneumonia precision, 95.09% recall, 96.90% F1-score, and 0.9929 ROC-AUC. These results indicate that the integrated preprocessing and Xception-based transfer learning framework provides strong and reproducible classification performance on the evaluated public dataset.]]>
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