Penerapan Saliency Maps dalam Explainable AI Untuk Deteksi Penyakit Paru-Paru pada Citra X-Ray Dada dengan Deep Learning
- Wahyu Reinaldy Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
-
Benny Sukma Negara
*
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru,
Indonesia
- Muhammad Irsyad Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Muhammad Affandes Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia
- Surya Agustian Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru, Indonesia (*) Corresponding Author
Abstract
Early identification of lung diseases is very important so that medical personnel can quickly provide first aid and further study the patient's condition. In this study, a model was developed to classify chest X-ray images of the lungs using the VGG16 architecture. These chest X-ray images were categorized into three groups: COVID-19, normal lungs, and pneumonia. A combination of hyperparameters, including a learning rate of 0.001, 50 epochs, and a batch size of 16, was used to train the model, achieved an accuracy of 96%. Several evaluation metrics, including precision, recall, f1-score, and confusion matrix, were used to assess the model. In addition, saliency map methods were used to visually interpret the model's prediction output and display the areas of the chest X-ray images that most influenced the model's decision-making. The saliency map visualization findings show that the model focuses its predictions on regions of the lungs associated with the disease, which helps in understanding the algorithm's decision-making process.
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References
Arsenault, P. D., Wang, S., & Patenaude, J. M. (2025). A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting. ACM Computing Surveys, 57(10). https://doi.org/10.1145/3729531
Arun, N., Gaw, N., Singh, P., Chang, K., Aggarwal, M., Chen, B., Hoebel, K., Gupta, S., Patel, J., Gidwani, M., Adebayo, J., Li, M. D., & Kalpathy-Cramer, J. (2021). Assessing the trustworthiness of saliency maps for localizing abnormalities in medical imaging. Radiology: Artificial Intelligence, 3(6). https://doi.org/10.1148/ryai.2021200267
Azizah, F. N., & Juniati, D. (2021). Analisis Jenis Penyakit Paru-Paru Berdasarkan Chest X-Ray Menggunakan Metode Fuzzy C-Means. MATHunesa: Jurnal Ilmiah Matematika, 9(2), 322–331. https://doi.org/10.26740/mathunesa.v9n2.p322-331
Fauziyyah, L. N., Negara, B. S., Irsyad, M., Iskandar, I., & Yanto, F. (2025). Interpreting Lung Disease Detection from Chest X-rays Using Layer-wise Relevance Propagation (LRP). Journal of Artificial Intelligence and Software Engineering, 5(2), 697–708. https://doi.org/10.30811/jaise.v5i2.7043
Gupta, J., Pathak, S., & Kumar, G. (2022). Deep Learning (CNN) and Transfer Learning: A Review. Journal of Physics: Conference Series, 2273(1). https://doi.org/10.1088/1742-6596/2273/1/012029
Idhom, M., Prasetya, D. A., Riyantoko, P. A., Fahrudin, T. M., & Sari, A. P. (2023). Pneumonia Classification Utilizing VGG-16 Architecture and Convolutional Neural Network Algorithm for Imbalanced Datasets. TIERS Information Technology Journal, 4(1), 73–82. https://doi.org/10.38043/tiers.v4i1.4380
Jain, N., Choudhury, A., Sharma, J., Kumar, V., De, D., & Tiwari, R. (2020). A review of novel coronavirus infection (Coronavirus Disease-19). Global Journal of Transfusion Medicine, 5(1), 22. https://doi.org/10.4103/gjtm.gjtm_24_20
Karar, M. E., Hemdan, E. E. D., & Shouman, M. A. (2021). Cascaded deep learning classifiers for computer-aided diagnosis of COVID-19 and pneumonia diseases in X-ray scans. Complex and Intelligent Systems, 7(1), 235–247. https://doi.org/10.1007/s40747-020-00199-4
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022). Transfer learning for medical image classification: a literature review. BMC Medical Imaging, 22(1), 1–13. https://doi.org/10.1186/s12880-022-00793-7
Kim, S. C., & Cho, Y. S. (2022). Predictive System Implementation to Improve the Accuracy of Urine Self-Diagnosis with Smartphones: Application of a Confusion Matrix-Based Learning Model through RGB Semiquantitative Analysis. Sensors, 22(14). https://doi.org/10.3390/s22145445
M.Kranthi, S.Sailaja, & E.V.N.Jyothi. (2024). Deep Learning Approaches for Medical Image Processing in the Big Data Era. International Journal of Scientific Methods in Computational Science and Engineering, 01(01), 24–31. https://doi.org/10.58599/ijsmcse.2024.1108
Ma’ruf, M. drg. O. P. M. dr. A. (2020). Profil Kesehatan Indonesia 2020.
Muliani, S., Sukma Negara, B., Irsyad, M., & Iskandar, I. (2025). Application of Shapley Additive Explanations (SHAP) in Deep Learning for Lung Disease Detection Using X-ray Images. Journal of Artificial Intelligence and Software Engineering, 5(2), 709–719. https://doi.org/10.30811/jaise.v5i2.7044
Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Van Le, H., Tran, V. Q., Prakash, I., & Pham, B. T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021. https://doi.org/10.1155/2021/4832864
Rahman, M. M., Matsuo, K., Matsuzaki, S., & Purushotham, S. (2021). DeepPseudo: Pseudo Value Based Deep Learning Models for Competing Risk Analysis. 35th AAAI Conference on Artificial Intelligence, AAAI 2021, 1, 479–487. https://doi.org/10.1609/aaai.v35i1.16125
Saturi, S., & Banda, S. (2024). Advanced Lung Disease Detection and Classification Using Ge-U-Net-ODLwith Gabor Filters and Entropy-Based Feature Selection. Journal of Sensors, IoT & Health Sciences, 2(2), 69–86. https://doi.org/10.69996/jsihs.2024011
Shahzad, A., Arshed, M. A., Liaquat, F., Tanveer, M., Hussain, M., & Alamdar, R. (2022). Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures. VAWKUM Transactions on Computer Sciences, 10(2), 34–44. https://doi.org/10.21015/vtcs.v10i2.1271
Shastri, S., Kansal, I., Kumar, S., Singh, K., Popli, R., & Mansotra, V. (2022). CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks. Health and Technology, 12(1), 193–204. https://doi.org/10.1007/s12553-021-00630-x
Simonyan, K., Vedaldi, A., & Zisserman, A. (2014). Deep inside convolutional networks: Visualising image classification models and saliency maps. 2nd International Conference on Learning Representations, ICLR 2014 - Workshop Track Proceedings, 1–8.
Sofiyah, W., Negara, B. S., Irsyad, M., Iskandar, I., & Yanto, F. (2025). Lung Disease Detection Using Gradient-Weighted Class Activation Mapping (Grad-CAM). Journal of Artificial Intelligence and Software Engineering, 5(2), 720–730. https://doi.org/10.30811/jaise.v5i2.7041
Subhash, B. (2022). Explainable AI: Saliency Maps. Medium.
Wollek, A., Graf, R., Čečatka, S., Fink, N., Willem, T., Sabel, B. O., & Lasser, T. (2023). Attention-based Saliency Maps Improve Interpretability of Pneumothorax Classification. Radiology: Artificial Intelligence, 5(2). https://doi.org/10.1148/ryai.220187
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