<![CDATA[Pneumonia remains one of the leading causes of morbidity and mortality worldwide, particularly in regions with limited access to diagnostic facilities. Chest X-ray (CXR) imaging is widely used for pneumonia detection; however, manual interpretation can be time-consuming and prone to variability among radiologists. This study proposes an optimized Mobile SE-CNN architecture that integrates Mobile Inverted Bottleneck Convolution (MBConv) and Squeeze-and-Excitation (SE) mechanisms to improve feature representation while maintaining computational efficiency. The model was trained and evaluated using the COVID-19 Radiography Database consisting of four classes: COVID-19, Lung Opacity, Viral Pneumonia, and Normal. Experimental results show that the proposed model achieved a test accuracy of 93.58% with a macro-average F1-score of 94.17%. Compared with the baseline CNN model, the proposed architecture improves classification accuracy by 3.69% while reducing the number of parameters by approximately 99.62%, using only 40,606 parameters and a total size of approximately 0.15 MB. These results demonstrate that the proposed Mobile SE-CNN achieves an effective balance between diagnostic performance and computational efficiency, making it suitable for deployment in mobile or embedded medical diagnostic systems.]]>
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