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An Improved ResNet-50 Based on Squeeze and Excitation for Lung Tuberculosis Classification
Corresponding Author(s) : Lakui Johary
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 11, No. 4, November 2026 (Article in Progress)
Abstract
Tuberculosis remains a major global health problem due to its high mortality rate and the difficulty of early diagnosis from medical imaging. Computed tomography (CT-scan) provides detailed visualization of lung structures; however, distinguishing between normal and tuberculosis patterns is challenging due to subtle and complex features. In this study, an improved deep learning model based on ResNet-50 integrated with a Squeeze-and-Excitation (SE) mechanism was proposed to enhance classification performance. A private CT-scan dataset collected from Klinik Utama Centrum, Semarang, was utilized, consisting of imbalanced classes that were addressed through data augmentation applied specifically to the tuberculosis class. The images were resized to 224 × 224 pixels and divided into training and testing sets with an 80:20 ratio. The model was trained using transfer learning with the Adam optimizer and evaluated using confusion matrix-based metrics. The experimental results showed that the proposed model achieved an accuracy of 94.55% and a macro F1-score of 0.9545, outperforming the standard ResNet-50 model. The confusion matrix analysis indicated a significant reduction in misclassification, particularly in tuberculosis detection. Furthermore, single-prediction testing on newly collected 2026 data demonstrated perfect classification results without any misclassification, which were clinically validated by a CT-scan specialist. These findings confirm that the integration of the SE mechanism improves feature representation and enhances model robustness, making it a reliable approach for automated tuberculosis classification from CT-scan images.
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M. Tsuneki, “Deep learning models in medical image analysis,” J. Oral Biosci., vol. 64, no. 3, pp. 312–320, Sep. 2022, doi: 10.1016/j.job.2022.03.003.
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L. Venkataramana, D. V. V. Prasad, S. Saraswathi, C. M. Mithumary, R. Karthikeyan, and N. Monika, “Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques,” Med. Biol. Eng. Comput., vol. 60, no. 9, pp. 2681–2691, Sep. 2022, doi: 10.1007/s11517-022-02632-x.
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