This study aims to improve the reliability of pneumonia detection from chest X-ray images by addressing the instability and performance variability observed in conventional CNNs, particularly the original Xception architecture, under different training conditions. An improved Xception-based model (IXCEP) is proposed, in which the Entry, Middle, and Exit flows are redesigned using enhanced separable convolutions and skip connections. The model is evaluated on a single pneumonia dataset using both a train/validation split and 5-fold cross-validation, considering different learning rates and numbers of epochs, with additional ablation experiments to assess the contribution of each modified flow. Experimental results show that IXCEPfull consistently outperforms the original Xception, achieving accuracy ranges of 88. 1–98. 1% (30 epochs, LR = 0. 01), 94. 5–97. 9% (30 epochs, LR = 0. 001), 96. 0–99. 8% (100 epochs, LR = 0. 01), and 87. 9–99. 0% (100 epochs, LR = 0. 001), with markedly reduced variability across folds. Ablation analysis reveals that the optimized Entry and Middle flows yield the most stable performance, reaching accuracies of 98. 6–99. 3%, whereas the Exit-only configuration shows higher sensitivity to training conditions. In contrast, the original Xception exhibits strong instability, with accuracy ranging from 51. 4% to 93. 8% across folds. Additional results, including F1-score values of up to 99. 8% and AUC values between 98. 8% and 100%, supported by Friedman and Iman–Davenport statistical tests, confirm the statistical significance of the improvements. Grad-CAM visualizations further demonstrate that IXCEP focuses on clinically relevant lung regions. Overall, these findings recommend IXCEP as a more stable and reliable alternative to the original Xception for pneumonia detection from chest radiographs.
Taib et al. (Thu,) studied this question.