Main Article Content
Abstract
Various types of lung diseases affect the human respiratory system, with pneumonia, tuberculosis, and Covid-19 being among the most common. Early detection plays a crucial role in improving treatment outcomes and reducing mortality rates. Chest X-ray imaging is one of the most widely used diagnostic methods; however, it typically relies on manual interpretation by medical professionals, which can be time-consuming and prone to inconsistencies. This study aims to apply the Convolutional Neural Network (CNN) method as an automated approach to classify chest X-ray images of lung conditions. The dataset consists of 460 X-ray images for each category: normal, pneumonia, tuberculosis, and Covid-19. The CNN model was trained using an input shape of 224×224 pixels, a 3×3 filter size, and 5 epochs. Evaluation results showed that the model achieved 97% accuracy on the validation and 93% on the testing data. These findings highlight the potential of CNN in supporting automated diagnosis of lung diseases. In the future, this technology is expected to assist healthcare professionals in delivering faster and more accurate diagnoses, particularly in areas with limited access to radiology experts. Moreover, this innovation aligns with Sustainable Development Goal (SDGs) 3: Good Health and Well-being, by promoting early detection, timely treatment, and more equitable access to quality healthcare services.
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Copyright (c) 2025 Danang Bagus Wibowo, Achmad Fauzan
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D. Djojodibroto, Respirologi (Respiratory Medicine). Jakarta: EGC, 2014.
Yuliana, “Corona virus diseases (Covid-19): Sebuah Tinjauan Literatur,” Wellness and Healty Magazine, vol. 2, no. 1, p. 187, Feb. 2020, [Online]. Available: https://wellness.journalpress.id/wellness
Dinas Kesehatan Provinsi Bali, “Profil Kesehatan Provinsi Bali Tahun 2016,” Bali, 2017.
I. Septiyanti, M. A. Khalif, and E. D. Anwar, “Analisis Dosis Paparan Radiasi Pada General X-Ray II Di Instalasi Radiologi Rumah Sakit Muhammadiyah Semarang,” Jurnal Imejing Diagnostik (JImeD), vol. 6, no. 2, pp. 96–102, Jul. 2020, doi: 10.31983/jimed.v6i2.5858.
A. H. Fauziyah, “Deteksi Pneumonia Pada Anak-Anak Dari Citra X-Ray Berbasis Convolutional Neural Network,” Institut Teknologi Sepuluh Nopember, 2020.
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I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
K. Xu, D. Feng, and H. Mi, “Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image,” Molecules, vol. 22, no. 12, p. 2054, Nov. 2017, doi: 10.3390/molecules22122054.
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P. K. Choudhary and H. N. Nagaraja, Measuring Agreement. Wiley, 2017. doi: 10.1002/9781118553282.
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K. P. Danukusumo, “Implementasi Deep Learning Menggunakan Convolutional Neural Network untuk Klasifikasi Citra Candi Berbasis GPU,” Universitas Atma Jaya Yogyakarta, Yogyakarta, 2017.
K. R. Bokka, S. Hora, T. Jain, and M. Wambugu, Deep Learning for Natural Language Processing: Solve your natural language processing problems with smart deep neural networks, 1st ed. Packt Publishing Ltd., 2019.
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C. E. Nwankpa, W. I. Ijomah, A. Gachagan, and S. Marshall, “Activation Functions: Comparison of trends in Practice and Research for Deep Learning,” in 2nd International Conference on Computational Sciences and Technologies, MUET Jamshoro, Dec. 2020. doi: 10.48550/arXiv.1811.03378.
S. Sumahasan, “Object Detection using Deep Learning Algorithm CNN,” Int J Res Appl Sci Eng Technol, vol. 8, no. 7, pp. 1578–1584, Jul. 2020, doi: 10.22214/ijraset.2020.30594.
M. Muntean and F.-D. Militaru, “Metrics for Evaluating Classification Algorithms,” 2023, pp. 307–317. doi: 10.1007/978-981-19-6755-
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N. S. Akbar, T. Zamir, J. Akram, T. Noor, and T. Muhammad, “Simulation of hybrid boiling nano fluid flow with convective boundary conditions through a porous stretching sheet through Levenberg Marquardt artificial neural networks approach,” Int J Heat Mass Transf, vol. 228, p. 125615, Aug. 2024, doi: 10.1016/j.ijheatmasstransfer.2024.125615.
M. Vakalopoulou, S. Christodoulidis, N. Burgos, O. Colliot, and V. Lepetit, “Deep Learning: Basics and Convolutional Neural Networks (CNNs),” 2023, pp. 77–115. doi: 10.1007/978-1-0716-3195-9_3.
M. Fluorida Fibrianda and A. Bhawiyuga, “Analisis Perbandingan Akurasi Deteksi Serangan Pada Jaringan Komputer Dengan Metode Naïve Bayes Dan Support Vector Machine (SVM),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 2, no. 9, pp. 3112–3123, Sep. 2018, [Online]. Available: http://j-ptiik.ub.ac.id.