Optimized Mobile SE-CNN for Pneumonia Detection Using Chest X-Ray Images

Baiq Anggita Arsya Rahmatin; I Gede Pasek Suta  Wijaya; Ario Yudo Husodo; Murizah Kassim

doi:10.38043/tiers.v7i1.7521

Authors

Baiq Anggita Arsya Rahmatin University of Mataram, Indonesia
I Gede Pasek Suta Wijaya University of Mataram, Indonesia
Ario Yudo Husodo University of Mataram, Indonesia
Murizah Kassim Universiti Teknologi MARA , Malaysia

DOI:

https://doi.org/10.38043/tiers.v7i1.7521

Keywords:

pneumonia detection, chest x-ray, convolutional neural network, MBConv, squeeze-and-excitation, deep learning, medical image classification

Abstract

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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Author Biographies

Baiq Anggita Arsya Rahmatin, University of Mataram, Indonesia

Department of Informatics Engineering, University of Mataram, NTB, Indonesia

I Gede Pasek Suta Wijaya , University of Mataram, Indonesia

Department of Informatics Engineering, University of Mataram, NTB, Indonesia

Ario Yudo Husodo, University of Mataram, Indonesia

Department of Informatics Engineering, University of Mataram, NTB, Indonesia

Murizah Kassim, Universiti Teknologi MARA , Malaysia

Institute for Big Data Analytics and Artificial Intelligence (IBDAAI), Universiti Teknologi MARA,Shah Alam, Selangor, Malaysia and Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

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