Automated Mycobacterium tuberculosis Detection in Multivariant Digitized Ziehl–Neelsen Staining Using Faster R‐CNN Method.
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and remains a major public health concern in Indonesia. One of the most widely used diagnostic methods is the microscopic examination of sputum smears stained using the Ziehl–Neelsen technique. However, manual identifica...
| Publicado en: | International Journal of Biomedical Imaging Vol. 2026; pp. 1 - 11 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/21/2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191010165&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191010165 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16874188 1WZI jtl: International Journal of Biomedical Imaging issn: 16874188 maglogo: N pubinfo: dt: 1/21/2026 vid: 2026 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 191010165 191010165 191010165 10.1155/ijbi/6692222 191010165 ppf: 1 ppct: 10 formats: tig: atl: Automated Mycobacterium tuberculosis Detection in Multivariant Digitized Ziehl–Neelsen Staining Using Faster R‐CNN Method. aug: au: Rulaningtyas, Riries Bilhaq, Fashalli Giovi Kusumaningrum, Deby Eric, Ronald Ittaqillah, Sayyidul Istighfar Trilaksana, Herri Widhyatmoko, Dicky Bagus Joseph, Annie Anak Ahmad, Irfan affil: Biomedical Engineering Study Program,, Department of Physics,, Faculty of Science and Technology,, Universitas Airlangga,, Surabaya, East Java,, Indonesia, unair.ac.id sug: subj: Automation, Laboratory Mycobacterium Tuberculosis Analysis Tuberculosis Diagnosis Staining and Labeling Convolutional Neural Networks Deep Learning Detection Algorithms Diagnosis, Computer Assisted Sputum Microbiology Human Indonesia Software Data Analysis, Computer Assisted Data Collection, Computer Assisted Sensitivity and Specificity Evaluation Precision Evaluation Comparative Studies Programming Languages Experimental Studies Microscopy Descriptive Statistics ab: Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis and remains a major public health concern in Indonesia. One of the most widely used diagnostic methods is the microscopic examination of sputum smears stained using the Ziehl–Neelsen technique. However, manual identification of TB bacteria presents significant challenges, particularly due to staining thickness variations that lead to inconsistent color intensities, making visual detection difficult and often subjective. This study is aimed at developing an automated TB bacteria detection system using deep learning, specifically the Faster R‐CNN algorithm with ResNet‐50 layers. The system is implemented using the Python programming language and the TensorFlow Object Detection API. We incorporated data augmentation in the form of random rotation, random flipping, and color processing such as hue variation, saturation stretching, brightness stretching, and exposure stretching. Experimental results show that the proposed model achieves an accuracy of 88%, with a precision of 94%, recall of 93%, and an F1‐score of 94%. The model outputs annotated images indicating the locations of TB bacteria, which can assist medical professionals in the diagnostic process. These findings demonstrate the potential of deep learning–based approaches in automating TB detection, particularly in healthcare settings with limited human resources. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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