Identification of Neisseria gonorrhoeae Bacteria Using a Convolutional Neural Network (CNN) Based on Image Classification
##plugins.themes.EpsilonTheme.article.main##
Haris Maulana*
Mudyawati Kamaruddin
Agus Suyanto
Auliyaur Rabban
Neisseria gonorrhoeae (gonococcus) is the primary bacterium responsible for the sexually transmitted infection gonorrhea, which is transmitted through sexual contact. Traditional identification methods, such as Polymerase Chain Reaction (PCR), are still widely used but have limitations in terms of cost, time, and the need for multiple reagents. This study aims to develop a faster and more efficient identification method using Artificial Intelligence (AI) through a Convolutional Neural Network (CNN) approach based on the Inception V3 architecture. The dataset used consists of 84 JPEG images, comprising 42 images of Neisseria gonorrhoeae and 42 non-Neisseria images. The model was trained using 50 epochs with an early stopping mechanism, which optimally halted at epoch 25, achieving a training accuracy of 94.74% and a validation accuracy of 100%. The resulting model achieved 96% classification accuracy, correctly identifying all 8 positive and 4 negative test images. These findings indicate that CNN based on Inception V3 is effective in classifying Neisseria gonorrhoeae images and has strong potential as a fast, accurate, and efficient diagnostic alternative.
Ahmed, S. R., Befano, B., Egemen, D., Rodriguez, A. C., Desai, K. T., Jeronimo, J., Ajenifuja, K. O., Clark, C., Perkins, R., & Campos, N. G. (2025). Generalizable deep neural networks for image quality classification of cervical images. Scientific Reports, 15(1), 6312. https://doi.org/10.1038/s41598-025-90024-0
Arbefeville, S. S., Timbrook, T. T., & Garner, C. D. (2024). Evolving strategies in microbe identification—A comprehensive review of biochemical, MALDI-TOF MS and molecular testing methods. Journal of Antimicrobial Chemotherapy, 79(Supplement_1), i2–i8. https://doi.org/10.1093/jac/dkae275
Badaring, D. R., Fiqriansyah, M., & Bahri, A. (2020). Identifikasi Morfologi Mikroba pada Ruangan Water Closet Jurusan Biologi Universitas Negeri Makassar. Prosiding Seminar Nasional Biologi FMIPA UNM. Makassar: Program Studi Biologi, Universitas Negeri Makassar, 161–168.
Bohr, A., & Memarzadeh, K. (2020). The rise of artificial intelligence in healthcare applications. In Artificial Intelligence in healthcare (pp. 25–60). Elsevier. https://doi.org/10.1016/B978-0-12-818438-7.00002-2
Carroll, K. C., Pfaller, M. A., Karlowsky, J., Landry, M. L., McAdam, A. J., Patel, R., & Pritt, B. S. (2024). Manual of clinical microbiology, 4 Volume Set. John Wiley & Sons.
Collins, S. P., Pope, R. K., Scheetz, R. W., Ray, R. I., Wagner, P. A., & Little, B. J. (1993). Advantages of environmental scanning electron microscopy in studies of microorganisms. Microscopy Research and Technique, 25(5‐6), 398–405.
Daley, R. J., & Hobbie, J. E. (1975). Direct counts of aquatic bacteria by a modified epifluorescence technique 1. Limnology and Oceanography, 20(5), 875–882. https://doi.org/10.4319/lo.1975.20.5.0875
Embleton, K. V, Gibson, C. E., & Heaney, S. I. (2003). Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method. Journal of Plankton Research, 25(6), 669–681. https://doi.org/10.1093/plankt/25.6.669
Franco-Duarte, R., Černáková, L., Kadam, S., S. Kaushik, K., Salehi, B., Bevilacqua, A., Corbo, M. R., Antolak, H., Dybka-Stępień, K., & Leszczewicz, M. (2019). Advances in chemical and biological methods to identify microorganisms—from past to present. Microorganisms, 7(5), 130. https://doi.org/10.3390/microorganisms7050130
Franiko, R., Suhendra, E. J., & Thevany, T. (2025). Innovation in Urology Telerobotic Surgery in Improving Treatment Effectiveness and Safety through a Systematic Journal Review. PHARMACOLOGY, MEDICAL REPORTS, ORTHOPEDIC, AND ILLNESS DETAILS, 4(1), 41–53. https://doi.org/10.55047/comorbid.v4i1.1658
Gray, T. R. G. (1967). Stereoscan electron microscopy of soil microorganisms. Science, 155(3770), 1668–1670.
Khovidhunkit, S. P., Phosri, K., Thanathornwong, B., Rungraungrayabkul, D., Poomrittigul, S., & Treebupachatsakul, T. (2025). Performance of deep learning models for the classification and object detection of different oral white lesions using photographic images. Scientific Reports, 15(1), 30834. https://doi.org/10.1038/s41598-025-14450-w
Kim, H. E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022). Transfer learning for medical image classification: a literature review. BMC Medical Imaging, 22(1), 69. https://doi.org/10.1186/s12880-022-00793-7
Knapič, S., Malhi, A., Saluja, R., & Främling, K. (2021). Explainable artificial intelligence for human decision support system in the medical domain. Machine Learning and Knowledge Extraction, 3(3), 740–770. https://doi.org/10.3390/make3030037
Kumar, Y., Koul, A., Singla, R., & Ijaz, M. F. (2023). Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. Journal of Ambient Intelligence and Humanized Computing, 14(7), 8459–8486. https://doi.org/10.1007/s12652-021-03612-z
Leboffe, M. J., & Pierce, B. E. (2011). Photographic Atlas for the microbiology laboratory. 4th editio. USA: Douglas N. Morton.
LeCun, Y., & Bengio, Y. (1998). Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks.
Locey, K. J., & Lennon, J. T. (2016). Scaling laws predict global microbial diversity. Proceedings of the National Academy of Sciences, 113(21), 5970–5975. https://doi.org/10.1073/pnas.1521291113
Mai, D.-T., & Ishibashi, K. (2021). Small-scale depthwise separable convolutional neural networks for bacteria classification. Electronics, 10(23), 3005.
Maltby, V. E., Lea, R. A., Graves, M. C., Sanders, K. A., Benton, M. C., Tajouri, L., Scott, R. J., & Lechner-Scott, J. (2018). Genome-wide DNA methylation changes in CD19+ B cells from relapsing-remitting multiple sclerosis patients. Scientific Reports, 8(1), 17418. https://doi.org/10.1038/s41598-018-35603-0
Meyer, T., & Buder, S. (2020). The laboratory diagnosis of Neisseria gonorrhoeae: current testing and future demands. Pathogens, 9(2), 91. https://doi.org/10.3390/pathogens9020091
Naik, N., Hameed, B. M., Shetty, D. K., Swain, D., Shah, M., Paul, R., Aggarwal, K., Ibrahim, S., Patil, V., & Smriti, K. (2022). Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Frontiers in Surgery, 9, 862322.
Nugraini, V. F. (2024). Klasifikasi Penyakit Kulit Nevus Menggunakan Metode Convolutional Neural Network Dengan Arsitektur Inception V3. Universitas Islam Sultan Agung Semarang.
Nurtanio, I., Bustamin, A., Yohannes, C., & Handoyo, A. T. (2022). Multi Classification of Bacterial Microscopic Images Using Inception V3. ILKOM Jurnal Ilmiah, 14(1), 80–90. https://doi.org/10.33096/ilkom.v14i1.1121.80-90
Pillai, N., Ramkumar, M., & Nanduri, B. (2022). Artificial intelligence models for zoonotic pathogens: a survey. Microorganisms, 10(10), 1911. https://doi.org/10.3390/microorganisms10101911
Pratiwi, K. N. F., Mastra, N., & Sofi Yanty, J. (2020). Identifikasi Bentuk Bakteri Dari Swab Tangan Petugas Pengangkut Sampah Di Desa Dalung, Kecamatan Kuta Utara, Kabupaten Badung. Poltekkes Denpasar Jurusan Teknologi Laboratorium Medis.
Quillin, S. J., & Seifert, H. S. (2018). Neisseria gonorrhoeae host adaptation and pathogenesis. Nature Reviews Microbiology, 16(4), 226–240. https://doi.org/10.1038/nrmicro.2017.169
Sawatzky, P., Martin, I., Galarza, P., Carvallo, M. E. T., Rodriguez, P. A., Cruz, O. M. S., Hernandez, A. L., Martinez, M. F., Borthagaray, G., & Payares, D. (2018). Quality assurance for antimicrobial susceptibility testing of Neisseria gonorrhoeae in Latin American and Caribbean countries, 2013–2015. Sexually Transmitted Infections, 94(7), 479–482. https://doi.org/10.1136/sextrans-2017-053502
Soe, N. N., Yu, Z., Latt, P. M., Lee, D., Ong, J. J., Ge, Z., Fairley, C. K., & Zhang, L. (2024). Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata. BMC Medicine, 22(1), 296. https://doi.org/10.1186/s12916-024-03512-x
Zieliński, B., Plichta, A., Misztal, K., Spurek, P., Brzychczy-Włoch, M., & Ochońska, D. (2017). Deep learning approach to bacterial colony classification. PloS One, 12(9), e0184554. https://doi.org/10.1371/journal.pone.0184554
