<![CDATA[Deep learning-based chest X-ray (CXR) classification frequently encounters bottlenecks due to the scarcity of labeled medical data and imbalanced class distributions. This study aims to implement a semi-supervised learning (SSL) approach utilizing the SoftMatch algorithm with a DenseNet-121 backbone for the multiclass classification of CXR images (Normal, COVID-19, and Pneumonia) under limited label conditions. SoftMatch is specifically selected for its capability to mitigate the quantity-quality trade-off through an adaptive pseudo-label soft-weighting mechanism. A dataset comprising 5,228 images is allocated via a stratified split into 70% training data, 10% validation data, and 20% testing data. Experiments are conducted across three labeled data proportion scenarios (5%, 10%, and 20%), each evaluated with and without Uniform Alignment. Evaluation metrics include accuracy, macro F1-score, confusion matrix, ROC-AUC, supported by visual interpretability analysis using Grad-CAM. The experimental results demonstrate that the model remains robust under the most critical scenario (5% labels), achieving an accuracy of 91.68% and a macro F1-score of 91.72% when integrating Uniform Alignment (UA), outperforming the scenario without UA, which records an accuracy of 90.73% and a macro F1-score of 90.82%. The best performance for the UA configuration is achieved in the 10% label scenario (accuracy 94.46%; macro F1-score 94.58%), while the peak overall performance is attained by the 20% label scenario without UA (accuracy 95.79%; macro F1-score 95.89%). These findings indicate that Uniform Alignment is effective in low-to-medium label conditions but does not consistently enhance performance at higher label proportions.]]>
