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Vol. 10, No. 3, August 2025

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Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models

https://doi.org/10.22219/kinetik.v10i3.2268
Achmad Bauravindah
Islamic University of Indonesia
Dhomas Hatta Fudholi
Islamic Universirty of Indonesia
Rima Tri Wahyuningrum
Universitas Trunojoyo Madura

Corresponding Author(s) : Achmad Bauravindah

baurav99@gmail.com

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 10, No. 3, August 2025

Abstract

Indonesia faces a critical shortage of radiologists, with only 1.2 radiologists per 100,000 individuals. This shortage leads to delays in diagnosing thoracic abnormalities such as pneumothorax, cardiomegaly, nodule/mass, consolidation, and infiltration. Chest X-ray (CXR) interpretation remains challenging due to overlapping radiological features, necessitating AI-assisted solutions. This study evaluates three lightweight deep learning models—MobileNetV2, ShuffleNetV2, and EfficientNetB0—for automated thoracic abnormality detection using the ChestX-ray8 dataset. We assessed model performance using accuracy, precision, recall, F1-score, and AUC-ROC, selecting the best model based on the highest per-fold F1-score. EfficientNetB0 emerged as the top-performing model, achieving a macro-average F1-score of 0.556 and AUC-ROC of 0.765, outperforming MobileNetV2 (0.494, 0.719) and ShuffleNetV2 (0.481, 0.713). Grad-CAM analysis revealed strong localization for pneumothorax and consolidation but misclassifications in cardiomegaly and nodule/mass detection due to poor feature differentiation. The findings highlight EfficientNetB0’s potential as an AI-assisted diagnostic tool for low-resource settings while also underscoring the need for segmentation-based pretraining and multi-scale feature extraction to enhance detection accuracy. Future work should focus on optimizing sensitivity to subtle abnormalities and ensuring clinical trust through improved interpretability techniques.

Keywords

Artificial Intelligence Healthcare Chest X-Ray Image Mobile Deep Learning Lung Abnormalities
Bauravindah, A., Fudholi, D. H., & Wahyuningrum, R. T. (2025). Efficient Thoracic Abnormalities Detection Using Mobile Deep Learning Models. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 10(3), 411-430. https://doi.org/10.22219/kinetik.v10i3.2268
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