Comparison of Deep Learning Methods for Detecting Tuberculosis Through Chest X-Rays
DOI:
10.47709/cnahpc.v6i3.4345Keywords:
Tuberculosis, Deep Learning, CNN, VGG-19, Histogram EqualizationDimension Badge Record
Abstract
Chronic diseases are the leading cause of death worldwide, accounting for 73% of deaths in 2020. Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis, is one of these diseases and has a significant impact on countries with a high TB burden due to a lack of radiologists and medical equipment. Early diagnosis of TB is crucial but challenging because of its similarity to lung cancer and the shortage of radiologists. A semi-automatic TB detection system is needed to support medical diagnosis and improve public health services. Deep learning technology, such as Convolutional Neural Networks (CNN), offers an effective solution for disease diagnosis with high accuracy. This study compares deep learning methods using an 8-layer CNN and VGG-19, both enhanced with Histogram Equalization (HE) for improved image quality. The study utilizes chest X-ray images of normal lungs and TB-affected lungs from Kaggle. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. Results indicate that the VGG-19 model outperforms the 8-layer CNN across all evaluation metrics, achieving an accuracy of 72.00% compared to 65.00% for the 8-layer CNN. VGG-19 also demonstrates better precision, recall, and F1-score, making it a more suitable choice for TB detection with enhanced image quality.
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References
Achmad, W. H., Saurina, N., Chamidah, N., & Rulaningtyas, R. (2023). Pemodelan Klasifikasi Tuberkulosis dengan Convolutional Neural Network. Prosiding Seminar Implementasi Teknologi Informasi Dan Komunikasi, 2(1), 9–15. https://doi.org/10.31284/p.semtik.2023-1.3989
Anissa Ollivia Cahya Pratiwi. (2023). Klasifikasi Jenis Anggur Berdasarkan Bentuk Daun Menggunakan Convolutional Neural Network Dan K-Nearest Neighbor. Jurnal Ilmiah Teknik Informatika Dan Komunikasi, 3(2), 201–224. http://journal.sinov.id/index.php/juitik/article/view/535
Bahri, S., Wajhillah, R., & Adiwisastra, M. F. (2021). Diagnosis of Pulmonary Tuberculosis Based on X-ray Image Using Convolutional Neural Network. Indonesian Journal on Computer and Information Technology, 6(2), 181–186. http://ejournal.bsi.ac.id/ejurnal/index.php/ijcit
Hartono, I., Noertjahyana, A., & Santoso, L. W. (2022). Deteksi Masker Wajah dengan Metode Convolutional Neural Network. Jurnal Infra. https://publication.petra.ac.id/index.php/teknik-informatika/article/view/12042
Hilmi, N., Wahyu, D., & Saputra, A. (2023). Edu Komputika Journal Implementasi HE, AHE, dan CLAHE Pada Metode Convolutional Neural Network untuk Identifikasi Citra X-Ray Paru-Paru Normal atau Terinfeksi Covid19. Edu Komputika, 10(1), 1–9. http://journal.unnes.ac.id/sju/index.php/edukom
Kholik, A. (2021). Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram. Jurnal Data Mining Dan Sistem Informasi, 2(2), 10. https://doi.org/10.33365/jdmsi.v2i2.1345
Magdalena, R., Saidah, S., Pratiwi, N. K. C., & Putra, A. T. (2021). Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN). Jurnal Edukasi Dan Penelitian Informatika (JEPIN), 7(3), 335. https://doi.org/10.26418/jp.v7i3.48195
Making, M. A., Banhae, Y. K., Aty, M. Y. V. B., Mau, Y., Abanit, Selasa, P., & Israfil. (2023). Analisa Faktor Pengetahuan Dan Sikap Dengan Perilaku Pencegahan Tb Paru Pada Kontak Serumah Selama Era New Normal Covid 19. Jurnal Penelitian Perawat Profesional, 5(1), 43–50.
Manurung, N. (2021). Peningkatan Pengetahuan Keluarga Penderita Tuberkulosis Tentang Perlunya Pendampingan Selama Pengobatan Dalam Meningkatkan Kepatuhan Di Puskesmas …. KREATIF: Jurnal Pengabdian Masyarakat …, 1(4), 42–48. https://journal.amikveteran.ac.id/index.php/kreatif/article/view/1368
Marcella, D., Yohannes, Y., & Devella, S. (2022). Klasifikasi Penyakit Mata Menggunakan Convolutional Neural Network Dengan Arsitektur VGG-19. Jurnal Algoritme, 3(1), 60–70. https://doi.org/10.35957/algoritme.v3i1.3331
Nihayatul Husna, I., Ulum, M., Kurniawan Saputro, A., Tri Laksono, D., & Neipa Purnamasari, D. (2022). Rancang Bangun Sistem Deteksi Dan Perhitungan Jumlah Orang Menggunakan Metode Convolutional Neural Network (CNN). Seminar Nasional Fortei Regional, 7, 2.
Noviyanti, L. K., Dwi Nugroho, K., & Dewa, S. A. (2023). “Pak Dede” (Program Aktualisasi Kader Deteksi Dini Depresi) Lansia Dengan Penyakit Tbc. Indonesia Mengabdi, 2(1), 24–33. https://doi.org/10.55080/jim.v2i1.136
Rahman, T., Khandakar, A., Kadir, M. A., Islam, K. R., Islam, K. F., Mazhar, R., Hamid, T., Islam, M. T., Kashem, S., Mahbub, Z. Bin, Ayari, M. A., & Chowdhury, M. E. H. (2020). Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization. IEEE Access, 8, 191586–191601. https://doi.org/10.1109/ACCESS.2020.3031384
Riti, Y. F., & Tandjung, S. S. (2022). Klasifikasi Covid-19 Pada Citra CT Scans Paru-Paru Menggunakan Metode Convolution Neural Network. Progresif: Jurnal Ilmiah Komputer, 18(1), 91. https://doi.org/10.35889/progresif.v18i1.784
Saputra, K., Taufik, I., Dharma, D. F., & Hidayat, M. (2021). Analisis Perbaikan Kualitas Citra Menggunakan CLAHE dan HE Pada Citra X-Ray Covid-19 dan Pneumonia. 6(2), 97–104.
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