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 (Forum Kerjasama Pendidikan Tinggi (FKPT), 2026)
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.