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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA
Wahyu Reinaldy
Universitas Islam Negeri Sultan Syarif Kasim Riau, Pekanbaru
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Penerapan Saliency Maps dalam Explainable AI Untuk Deteksi Penyakit Paru-Paru pada Citra X-Ray Dada dengan Deep Learning Wahyu Reinaldy; Benny Sukma Negara; Muhammad Irsyad; Muhammad Affandes; Surya Agustian
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9962

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.
Co-Authors Muhammad Affandes Muhammad Irsyad Negara, Benny Sukma Surya Agustian