Segmentation of Tuberculosis Bacteria: Evaluation and Comparison

Vanitha, V.; Angayarkanni, S. Pitchumani; Sathishkumar, M

doi:10.1109/csci54926.2021.00321

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2024

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Cited by 3 publications

References 15 publications

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Machine-learning methods for detecting tuberculosis in Ziehl-Neelsen stained slides: A systematic literature review

Tamura,

Llano,

Aristizábal

et al. 2024

Intelligent Systems with Applications

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Machine-learning methods for detecting tuberculosis in Ziehl-Neelsen stained slides: A systematic literature review

Tamura,

Llano,

Aristizábal

et al. 2024

Intelligent Systems with Applications

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Evaluation of Depth-wise Separable Convolution and Channel Attention Mechanism to Bacilli Segmentation

Soares,

Monteiro,

Pinheiro

et al. 2024

2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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AI-Based Analysis of Ziehl–Neelsen-Stained Sputum Smears for Mycobacterium tuberculosis as a Screening Method for Active Tuberculosis

Witarto,

Ceachi,

Popp

et al. 2024

Life

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Tuberculosis is the primary cause of death due to infection in the world. Identification of Mycobacterium tuberculosis in sputum is a diagnostic test, which can be used in screening programs—especially in countries with a high incidence of tuberculosis—to identify and treat those persons with the highest risk of disseminating the infection. We previously developed an algorithm which is able to automatically detect mycobacteria on tissue; in particular, our algorithm identified acid-fast bacilli on tissue with 100% specificity, 95.65% sensitivity, and 98.33% accuracy. We tested this algorithm on 1059 Ziehl–Neelsen-stained sputum smears to evaluate its results as a possible tool for screening. The results were displayed as a heat map of 32 × 32 pixel patches. Analysis of the positive patches revealed a good specificity (86.84%) and 100% sensitivity for patches with a level of confidence over 90; furthermore, the accuracy remained over 95% for all levels of confidence over 80, except the class (95–100]. The modest specificity is caused by the peculiarities of smears (uneven thickness, dust contamination, lack of coverslip). We will train the algorithm on sputum smears to increase the specificity to over 95%. However, as our algorithm showed no false negatives, it is suitable for screening.

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Segmentation of Tuberculosis Bacteria: Evaluation and Comparison

Cited by 3 publications

References 15 publications

Machine-learning methods for detecting tuberculosis in Ziehl-Neelsen stained slides: A systematic literature review

Machine-learning methods for detecting tuberculosis in Ziehl-Neelsen stained slides: A systematic literature review

Evaluation of Depth-wise Separable Convolution and Channel Attention Mechanism to Bacilli Segmentation

AI-Based Analysis of Ziehl–Neelsen-Stained Sputum Smears for Mycobacterium tuberculosis as a Screening Method for Active Tuberculosis

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