Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification

Authors

  • Adya Zizwan Putra Universitas Prima Indonesia
  • Reynaldi Prayugo Universitas Prima Indonesia
  • Rizki Mudrika Alfanda Siregar Universitas Prima Indonesia
  • Rizky Syabani Universitas Prima Indonesia
  • Allwin Simarmata Universitas Prima Indonesia

DOI:

10.33395/sinkron.v8i3.13723

Keywords:

Tuberculosis, Classification, CNN; Tranfer Learning, Deep Learning

Abstract

Tuberculosis (TB) is an infectious disease that can have serious effects on the lungs and is among the top 10 causes of death worldwide. This disease is caused by the transmission of Mycobacterium tuberculosis bacteria through the air when coughing or sneezing. Without treatment, pulmonary tuberculosis can result in permanent lung damage and can be life-threatening. Accurate and early diagnosis is crucial for effective treatment and control of the disease.The challenge lies in the accurate classification of tuberculosis from lung images, which is essential for timely diagnosis and treatment. Traditional diagnostic methods can be time-consuming and sometimes lack precision. To address this issue, this research aims to achieve high accuracy in classifying tuberculosis using the Convolutional Neural Network (CNN) algorithm through transfer learning methods. By utilizing visual images of tuberculosis-affected and normal lungs, we propose a solution that leverages advanced deep learning techniques to enhance diagnostic accuracy. This approach not only expedites the diagnostic process but also improves the reliability of tuberculosis detection, ultimately contributing to better patient outcomes and more effective disease management. The dataset applied consists of two labels: tuberculosis and normal. This dataset contains 4200 lung images of individuals with tuberculosis and normal lungs. By applying the transfer learning method, Transfer learning is a machine learning method where a pre-trained model is used as the starting point for a new, related task. it was found that the ResNet50 model achieved the highest accuracy at 99%, followed by InceptionV3 at 97%, and lastly, DenseNet121 at 91%.

GS Cited Analysis

Downloads

Download data is not yet available.

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

Ali, N. H., Abdulmunim, M. E., & Ali, A. E. (2021). Constructed model for micro-content recognition in lip reading based deep learning. Bulletin of Electrical Engineering and Informatics, 10(5), 2557–2565. https://doi.org/10.11591/eei.v10i5.2927

Anggiratih, E., Siswanti, S., Octaviani, S. K., & Sari, A. (2021). Klasifikasi Penyakit Tanaman Padi Menggunakan Model Deep Learning Efficientnet B3 dengan Transfer Learning. Jurnal Ilmiah SINUS, 19(1), 75. https://doi.org/10.30646/sinus.v19i1.526

Aras, S., Setyanto, A., & Rismayani. (2022). Deep Learning Untuk Klasifikasi Motif Batik Papua Menggunakan EfficientNet dan Trasnfer Learning. Insect (Informatics and Security): Jurnal Teknik Informatika, 8(1), 11–20. https://doi.org/10.33506/insect.v8i1.1865

Esdras Chaves, Caroline B. Gonçalves, Marcelo K. Albertini, Soojeong Lee, Gwanggil Jeon, and H. C. F. (2020). Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images. Appl. Opt, 59–17.

Fitroh, Q. A., & ’Uyun, S. (2022). Deep Transfer Learning to Deep Transfer Learning to Improve Classification Accuracy in Dermoscopic Images of Skin Cancer. Jurnal Nasional Teknik Elektro Dan Teknologi Informasi, 12(2), 78–84. https://journal.ugm.ac.id/v3/JNTETI/article/view/6502

Germas. (2021). Kementrian Kesehatan Republik Indonesia. Strategis Nasional Penanggulangan Tuberkulosis di Indonesia 2020-2024. https://tbindonesia.or.id/informasi/strategi-nasional/strategis-nasional-penanggulangan-tuberkulosis-di-indonesia-2020-2024/

Harahap, M., Anjelli, S. K., Sinaga, W. A. M., Alward, R., Manawan, J. F. W., & Husein, A. M. (2022). Classification of diabetic foot ulcer using convolutional neural network (CNN) in diabetic patients. Jurnal Infotel, 14(3), 196–202. https://doi.org/10.20895/infotel.v14i3.796

Harahap, M., Em Manuel Laia, Lilis Suryani Sitanggang, Melda Sinaga, Daniel Franci Sihombing, & Amir Mahmud Husein. (2022). Deteksi Penyakit Covid-19 Pada Citra X-Ray Dengan Pendekatan Convolutional Neural Network (CNN). Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 6(1), 70–77. https://doi.org/10.29207/resti.v6i1.3373

Iman, M., Arabnia, H. R., & Rasheed, K. (2023). A Review of Deep Transfer Learning and Recent Advancements. Technologies, 11(2), 1–14. https://doi.org/10.3390/technologies11020040

Kenali penyebab dan gejalaTBC paru yang menular. (2021). https://kesehatan.kontan.co.id/news/kenali-penyebab-dan-gejalatbc-paru-yang-menular

Kristini, T., & Hamidah, R. (2020). Potential for Pulmonary Tuberculosis Transmission to Patients Family. Indonesian Journal of Public Health, 15(1), 24.

Melni, A. (2024). HUBUNGAN FASE PENGOBATAN ANTI TUBERKULOSIS PARU DENGAN RASIO NEUTROFIL LIMFOSIT (RNL) DAN RASIO MONOSIT LIMFOSIT (RML) PADA PASIEN EFUSI PLEURA TB DI RSUD H. ABDUL MOELOEK PROVINSI LAMPUNG.

Naufal, M. F., & Kusuma, S. F. (2021). Pendeteksi Citra Masker Wajah Menggunakan CNN dan Transfer Learning. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(6), 1293. https://doi.org/10.25126/jtiik.2021865201

Oloko-Oba, M., & Viriri, S. (2020). Diagnosing tuberculosis using deep convolutional neural network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 12119 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-030-51935-3_16

Rochmawanti, O., Utaminingrum, F., & Bachtiar, F. A. (2021). Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(4), 805–814. https://doi.org/10.25126/jtiik.2021844441

S, P. P. R., Patil, A. A., Patil, A. A., Kad, A., Kharat, S., Parte, R. S., Patil, A. A., Patil, A. A., Kad, A., & Kharat, S. (2020). Non-Invasive Method for Diabetes Detection using CNN and SVM Classifier Abstract : International Journal of Research in Engineering, Science and Management, 3(3), 9–13.

Sathitratanacheewin, S., Sunanta, P., & Pongpirul, K. (2020). Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability. Heliyon, 6(8), e04614. https://doi.org/10.1016/j.heliyon.2020.e04614

Saxena, P., Maheshwari, A., & Maheshwari, S. (2021). Predictive Modeling of Brain Tumor: A Deep Learning Approach. Advances in Intelligent Systems and Computing, 1189, 275–285. https://doi.org/10.1007/978-981-15-6067-5_30

Sehat negeriku sehatlah bangasaku. (2022). https://sehatnegeriku.kemkes.go.id/baca/rilis-media/20220322/4239560/tahun-ini-kemenkes-rencanakan-skrining-tbc-besar-besaran/

Tuberkulosis (TBC), Kenali Gejala, Penyebab dan Cara Penularan. (2024). Mitra Keluarga. https://www.mitrakeluarga.com/artikel/artikel-kesehatan/tuberkulosis

Viera Valencia, L. F., & Garcia Giraldo, D. (2019). Perbandingan Model Klasifikasi Transfer Learning Convolutional Neural Network Tumor Otak Menggunakan Citra Magnetic Resonance Imaging. Angewandte Chemie International Edition, 6(11), 951–952., 2(1), 308–318.

Yan, P., Abdulkadir, A., Luley, P. P., Rosenthal, M., Schatte, G. A., Grewe, B. F., & Stadelmann, T. (2024). A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions. IEEE Access, 12(January), 3768–3789. https://doi.org/10.1109/ACCESS.2023.3349132

Zaidi, S. Z. Y., Akram, M. U., Jameel, A., & Alghamdi, N. S. (2022). A deep learning approach for the classification of TB from NIH CXR dataset. IET Image Processing, 16(3), 787–796. https://doi.org/10.1049/ipr2.12385

Downloads


Crossmark Updates

How to Cite

Putra, A. Z. ., Prayugo, R., Siregar, R. M. A., Syabani, R. ., & Simarmata, A. (2024). Implementation Transfer Learning on Convolutional Neural Network for Tubercolosis Classification. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 8(3), 1515-1525. https://doi.org/10.33395/sinkron.v8i3.13723

Issue

Vol. 8 No. 3 (2024): Research Artikel Volume 8 Issue 3, July 2024

Section

Articles

License

Copyright (c) 2024 Adya Zizwan Putra, Reynaldi Prayugo, Rizki Siregar, Rizky Syahbani, Allwin Simarmata

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Crossref
Scopus
Google Scholar
Europe PMC

Most read articles by the same author(s)