Open Access
E3S Web of Conf.
Volume 388, 2023
The 4th International Conference of Biospheric Harmony Advanced Research (ICOBAR 2022)
Article Number 02004
Number of page(s) 6
Section Big Data, Green Computing, and Information System
Published online 17 May 2023
  1. S. Sathitratanacheewin, P. Sunanta, K. Pongpirul, Deep learning for automated classification of tuberculosis-related chest x-ray: dataset distribution shift limits diagnostic performance generalizability, Heliyon 6, 8, e04614 https://doi.or g/10.1016/j.heliyon.2020.e04614 (2020) [CrossRef] [PubMed] [Google Scholar]
  2. World Health Organization (WHO), Global tuberculosis report 2021 (World Health Organization, Geneva, 2021a) [Google Scholar]
  3. World Health Organization (WHO), Tuberculosis country profile Indonesia, global tuberculosis report 2021 (World Health Organization, Geneva, 2021b) [Google Scholar]
  4. G. Fund, Current status of integrated community based TB service delivery and the global fund work plan to find missing TB cases, Indonesia National TB Program (n.d.) [Google Scholar]
  5. Y. Mahendradhata, L. Trisnantoro, S. Listyadewi, P. Soewondo, T. MArthias, P. Harimurti, J. Prawira, The republic of Indonesia health system review, Health Systems in Transition 7, 1, pp. 1-292 (2017) [Google Scholar]
  6. A. Noviyani, T. Nopsopon, K. P. Id, Variation of tuberculosis prevalence across diagnostic approaches and geographical areas of Indonesia, PLoS ONE 9, pp. 1–12 (2021) [Google Scholar]
  7. D. Pratiwi, D. D. Santika, B. Pardamean, An application of backpropagation artificial neural network method for measuring the severity of osteoarthritis, International J. Engineering & Technology 11, 3, pp. 102–105 (2011) [Google Scholar]
  8. F. Asadi, F. M. Putra, M. Indah Sakinatunnisa, F. Syafria, Okfalisa, I. Marzuki, Implementation of backpropagation neural network and blood cells imagery extraction for acute leukemia classification, Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017, pp. 106–110 8537755 (2018) [Google Scholar]
  9. T. W. Cenggoro, B. Mahesworo, A. Budiarto, J. Baurley, T. Suparyanto, B. Pardamean, Features importance in classification models for colorectal cancer cases phenotype in Indonesia, Procedia Computer Science 157, pp. 313–320 (2019) [CrossRef] [Google Scholar]
  10. F. Asadi, C. A. Chen, T.-W. Liu, F. Syafria, Acute leukemia (ALL and AML) classification using learning vector quantization (LVQ.1) with blood cell imagery extraction, International J. Modeling and Optimization 9, 3, pp. 171–176 http://dx.doi.or g/10.7763/IJMO.2019.V9.705 (2019) [CrossRef] [Google Scholar]
  11. R. Vinuesa, H. Azizpour, I. Leite, M. Balaam, V. Dignum, S. Domisch, A. Felländer, S. D. Langhans, M. Tegmark, F. Fuso Nerini, The role of artificial intelligence in achieving the sustainable development goals, Nature Communications 11, 1, pp. 1–10 (2020) [CrossRef] [PubMed] [Google Scholar]
  12. J. D. Morgenstern, L. C. Rosella, M. J. Daley, V. Goel, H. J. Schünemann, T. Piggott, “AI’s gonna have an impact on everything in society, so it has to have an impact on public health”: a fundamental qualitative descriptive study of the implications of artificial intelligence for public health, BMC Public Health 21, 1, pp. 1–14 (2021) [CrossRef] [PubMed] [Google Scholar]
  13. I. Yusuf, B. Pardamean, J. W. Baurley, A. Budiarto, U. A. Miskad, R. E. Lusikooy, A. Arsyad, A. Irwan, G. Mathew, I. Suriapranata, R. Kusuma, M. F. Kacamarga, T. W. Cenggoro, C. McMahan, C. Joyner, C. I. Pardamean, Genetic risk factors for colorectal cancer in multiethnic Indonesians, Scientific Reports 11, 1, pp. 1–10 1038/s41598-021-88805-4 (2021) [CrossRef] [PubMed] [Google Scholar]
  14. M. Mohammadi, A. Al-Fuqaha, S. Sorour, M. Guizani, Deep learning for IoT big data and streaming analytics: a survey, IEEE Communications Surveys and Tutorials 20, 4, pp. 2923–2960 2844341 (2018) [CrossRef] [Google Scholar]
  15. J. M. Puaschunder, The potential for artificial intelligence in healthcare, SSRN Electronic J. 6, 2, pp. 94–98 (2020) [Google Scholar]
  16. Daniel, T. W. Cenggoro, B. Pardamean, A systematic literature review of machine learning application in covid-19 medical image classification, International Conference on Computer Science and Computational Intelligence (ICCSCI), 978-1–6654 (2021) [Google Scholar]
  17. M. Koenigkam Santos, J. Raniery Ferreira Júnior, D. Tadao Wada, A. Priscilla Magalhães Tenório, M. Henrique Nogueira Barbosa, P. Mazzoncini De Azevedo Marques, Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine, Radiologia Brasileira 52, 6, pp. 387–396 19.0049 (2019) [CrossRef] [PubMed] [Google Scholar]
  18. N. Dominic, Daniel, T. W. Cenggoro, A. Budiarto, B. Pardamean, Transfer learning using inception-resnet-v2 model to the augmented neuroimages data for autism spectrum disorder classification, Communications in Mathematical Biology and Neuroscience, pp. 1–21 (2021) [Google Scholar]
  19. S. S. Panigoro, E. Listiyaningsih, I. Nurlaila, B. Mahesworo, A. A. Hidayat, A. Budiarto, D. Sudigyo, D. Amirullah, S. Simon, J. Baurley, B. Pardamean, Intronic variant of MUTYH gene exhibits a strong association with early onset of breast cancer susceptibility in Indonesian women population, Asian Pacific J. Cancer Prevention 22, 12, pp. 3985–3991 (2021) [CrossRef] [Google Scholar]
  20. Jimmy, T. W. Cenggoro, B. Pardamean, Systematic literature review: an intelligent pulmonary TB detection from chest x-rays, Proceedings of 2021 1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021, 978-1–6654, pp. 136–141 (2021) [Google Scholar]
  21. D. M. El-sherif, M. Abouzid, M. T. Elzarif, A. A. Ahmed, A. Albakri, M. M. Alshehri, Telehealth and artificial intelligence insights into healthcare during the covid-19 pandemic, MDPI-J. Healthcare 10, 2, pp. 1–15 0020385 (2022) [Google Scholar]
  22. Y. Zhang, Y. Weng, J. Lund, Applications of explainable artificial intelligence in diagnosis and surgery, MDPI-Diagnostics 12, 2, pp. 1-18 (2022) [Google Scholar]
  23. A. Rahmadi, I. Fasyah, D. Sudigyo, A. Budiarto, B. Mahesworo, A. A. Hidayat, B. Pardamean, Comparative study of predicted miRNA between Indonesia and China (Wuhan) SARS-CoV-2: a bioinformatics analysis, Genes and Genomics 43, 9, pp. 1079–1086 (2021) [CrossRef] [PubMed] [Google Scholar]
  24. H. H. Muljo, B. Pardamean, K. Purwandari, T. W. Cenggoro, Improving lung disease detection by joint learning with covid-19 radiography database, Communications in Mathematical Biology and Neuroscience, pp. 1–24 (2022) [Google Scholar]
  25. Bracaglia, HHS public access, Physiology & Behavior 176, 3, pp. 139–148 (2017) [CrossRef] [PubMed] [Google Scholar]
  26. B. Pardamean, T. W. Cenggoro, R. Rahutomo, A. Budiarto, E. K. Karuppiah, Transfer learning from chest x-ray pre-trained convolutional neural network for learning mammogram data, Procedia Computer Science 135, pp. 400–407 (2018) [CrossRef] [Google Scholar]
  27. Y. Sato, Y. Takegami, T. Asamoto, Y. Ono, T. Hidetoshi, R. Goto, A. Kitamura, S. Honda, A computer-aided diagnosis system using artificial intelligence for hip fractures, Multi-Institutional Joint Development Research, pp. 1-9 (2020) [Google Scholar]
  28. K. Murphy, S. S. Habib, S. M. A. Zaidi, S. Khowaja, A. Khan, J. Melendez, E. T. Scholten, F. Amad, S. Schalekamp, M. Verhagen, R. H. H. M. Philipsen, A. Meijers, B. van Ginneken, Computer aided detection of tuberculosis on chest radiographs: an evaluation of the CAD4TB v6 system, Scientific Reports 10, 1, pp. 1–12 (2020) [CrossRef] [PubMed] [Google Scholar]
  29. World Health Organization (WHO), Operational handbook on tuberculosis (World Health Organization, Geneva, 2020) [Google Scholar]
  30. World Health Organization, Chest radiography in tuberculosis, World Health Organization, 9789241 (Chest radiography in tuberculosis detection), pp. 1–44 (2016) [Google Scholar]
  31. World Health Organization (WHO), A toolkit to support the effective use of CAD for TB screening (World Health Organization, Geneva, 2021c) [Google Scholar]
  32. P. D. O. Davies, M. Pai, The diagnosis and misdiagnosis of tuberculosis, International J. Tuberculosis and Lung Disease 12, 11, pp. 1226–1234 (2008) [Google Scholar]
  33. I. Satia, S. Bashagha, A. Bibi, R. Ahmed, S. Mellor, F. Zaman, Assessing the accuracy and certainty in interpreting chest x-rays in the medical division, Clinical Medicine, J. Royal College of Physicians of London 13, 4, pp. 349–352 (2013) [Google Scholar]
  34. N. Woznitza, K. Piper, S. Burke, G. Bothamley, Chest x-ray interpretation by radiographers is not inferior to radiologists: a multireader, multicase comparison using JAFROC (jack-knife alternative free-response receiver operating characteristics) analysis, Academic Radiology 25, 12, pp. 1556–1563 (2018) [CrossRef] [PubMed] [Google Scholar]
  35. D. Godjali, B. Pardamean, E. Suzanna, Pengembangan sistem registrasi kanker Indonesia, Indonesian J. Cancer 6, 2, pp. 61–66 http://dx.doi. org/10.33371/ijoc.v6i2.170 (2012) [Google Scholar]
  36. A. Gani, M. P. Budiharsana, The consolidated report on Indonesia health sector review 2018 lidated-report-indonesia-health-sector-review-2018-bahasa-version-page (2019) [Google Scholar]
  37. A. D. Laksono, I. A. Ridlo, E. Ernawaty, Distribution analysis of doctors in Indonesia, J. Administrasi Kesehatan Indonesia 8, 1, pp. 29-39 (2020) [CrossRef] [Google Scholar]
  38. P. Putha, M. Tadepalli, B. Reddy, T. Raj, J. A. Chiramal, S. Govil, N. Sinha, M. KS, S. Reddivari, A. Jagirdar, P. Rao, P. Warier, Can artificial intelligence reliably report chest x-rays?: radiologist validation of an algorithm trained on 2.3 million x-rays, arXiv, 1807.07455, pp. 1–13 (2018) [Google Scholar]
  39. A. J. Codlin, T. P. Dao, L. N. Q. Vo, R. J. Forse, V. Van Truong, H. M. Dang, L. H. Nguyen, H. B. Nguyen, N. V. Nguyen, K. Sidney-Annerstedt, B. Squire, K. Lönnroth, M. Caws, Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis, Scientific Reports 11, 1, pp. 1–11 8-021-03265-0 (2021) [CrossRef] [PubMed] [Google Scholar]
  40. Z. Z. Qin, M. S. Sander, B. Rai, C. N. Titahong, S. Sudrungrot, S. N. Laah, L. M. Adhikari, E. J. Carter, L. Puri, A. J. Codlin, J. Creswell, Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems, Scientific Reports 9, 1, pp. 1–11 (2019) [CrossRef] [PubMed] [Google Scholar]
  41. R. C. Koesoemadinata, K. Kranzer, R. Livia, N. Susilawati, J. Annisa, N. N. M. Soetedjo, R. Ruslami, R. Philipsen, B. Van Ginneken, R. D. Soetikno, R. Van Crevel, B. Alisjahbana, P. C. Hill, Computer-assisted chest radiography reading for tuberculosis screening in people living with diabetes mellitus, International J. Tuberculosis and Lung Disease 22, 9, pp. 1088–1094 (2018) [CrossRef] [PubMed] [Google Scholar]
  42. Y. Li, J. Wu, Q. Wu, Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning, IEEE Access 7, pp. 21400–21408 (2019) [CrossRef] [Google Scholar]
  43. L. Hadjiiski, B. Sahiner, H.-P. Chan, Advances in CAD for diagnosis of breast cancer, Curr Opin Obstet Gynecol 18, 1, pp. 64–70 1097/01.gco.0000192965.29449.da (2010) [Google Scholar]
  44. D. Bardou, K. Zhang, S. M. Ahmad, Classification of breast cancer based on histology images using convolutional neural networks, IEEE Access 6, pp. 24680–24693 18.2831280 (2018) [CrossRef] [Google Scholar]
  45. A. M. Bogdanova, D. Gjorgjevikj, Computer-aided diagnosis of malign and benign brain tumors on MR images emre, Advances in Intelligent Systems and Computing 311, pp. 157–158 (2015) [CrossRef] [Google Scholar]
  46. S. Khan, M. Sajjad, T. Hussain, A. Ullah, A. S. Imran, A review on traditional machine learning and deep learning models for WBCs classification in blood smear images, IEEE Access 9, pp. 10657–10673 (2021) [CrossRef] [Google Scholar]
  47. S. Xiong, G. Wu, X. Fan, X. Feng, Z. Huang, W. Cao, X. Zhou, S. Ding, J. Yu, L. Wang, Z. Shi, MRI-based brain tumor segmentation using FPGA-accelerated neural network, BMC Bioinformatics 22, 1, pp. 1–15 (2021) [CrossRef] [PubMed] [Google Scholar]
  48. T. Rahman, A. Khandakar, M. A. Kadir, K. R. Islam, K. F. Islam, R. Mazhar, T. Hamid, M. T. Islam, S. Kashem, Z. Mahbub, M. A. Bin Ayari, M. E. H. Chowdhury, Reliable tuberculosis detection using chest x-ray with deep learning, segmentation and visualization, IEEE Access 8, pp. 191586–191601 (2020) [CrossRef] [Google Scholar]
  49. S. Rajaraman, S. Antani, Weakly labeled data augmentation for deep learning: a study on covid-19 detection in chest x-rays, Diagnostics 10, 6, pp. 1–17 358 (2020) [CrossRef] [PubMed] [Google Scholar]
  50. Y. Erdaw, E. Tachbele, Machine learning model applied on chest x-ray images enables automatic detection of covid-19 cases with high accuracy, International J. General Medicine 14, pp. 4923–4931 (2021) [CrossRef] [Google Scholar]

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