Automated tuberculosis screening using Zeihl Neelson image

Govindan, Lavanya; Padmasini, N.; Yacin, Mohamed

doi:10.1109/icetech.2015.7275031

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…In the literature, both unsupervised (such as k-means) and supervised (such as Bayesian classifier) methods have been used. Govindan et al provided an example of unsupervised learning-based segmentation in which they utilised k-means clustering in conjunction with decorrelation stretching to identify areas of interest [45]. Consequently, dilating and eroding morphological operators were required to close any broken edges in the final segmented image.…”

Section: Stochastic-based Approachesmentioning

confidence: 99%

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Zachariou,

Arandjelović,

Sloan

2023

BioMedInformatics

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Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.

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Section: Stochastic-based Approachesmentioning

confidence: 99%

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Zachariou,

Arandjelović,

Sloan

2023

BioMedInformatics

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“…Feature extraction is performed by using four features referring to Refs. [11,20,21]. Those are perimeter, area, eccentricity, and the maximum intensity value of each ARB candidate.…”

Section: Clusteringmentioning

confidence: 99%

“…A good quality sputum image can improve the success of the segmentation process. Some used techniques for the MTB segmentation process include k-means clustering [8,[10][11][12], Self-Organizing Map [13], Watershed Transformation [14], and Adaptive Signal Processing [15]. The color characteristics of MTB tend to differ from the background.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

Kurniawan

Muhimmah

Kurniawardhani

et al. 2019

CommIT (Communication and Information Technology) Journal

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The easily transmitted Tuberculosis (TB) disease is attributed to the fact that Mycobacterium Tuberculosis (MTB) bacteria/viruses can be transmitted through the air. One of the methods to screen the TB disease is by reading sputum slides. Sputum slides are colored sputum samples of TB patients placed on microscopic slides. However, TB disease microscopic analysis has some limitations since it requires high accuracy reading and well-trained health personnel to avoid errors in the process of interpretation. Furthermore, the number of TB patients in the Primary Health Care (PHC) and the process of manual calculation of bacteria in a field of view often complicate the decision-making in the screening process conducted by the medical staffs. In this paper, the researchers propose the use of Watershed Transformation and Fuzzy C-Means combination to help solve the problem. The researchers collect the photo shooting of three PHC in Indonesia with 55 images of sputum from different TB patients. The assessed results of the proposed method are compared with the opinions of three Microbiology doctors. The comparison shows Cohen’s Kappa Coefficient value of 0.838. It suggests that the proposed method can detect Acid Resistant Bacteria (ARB) although it needs some improvement to achieve higher accuracy.

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“…The screening of TB patients is accomplished using the several techniques, which includes computed tomography scan, magnetic resonance imaging, ultrasound scan, chest X‐ray, Mantoux test, nucleic acid amplification test, biological culture, interferon‐γ test, and sputum smear microscopy. From the above tests, two tests, Sputum smear microscopy, and biological culture, are most commonly used for detecting TB 7 …”

Section: Introductionmentioning

confidence: 99%

“…The burden of TB disease is reduced by introducing automated bacilli detection by adapting ZN image. The patients with TB positive are easily identified using automated tuberculosis screening systems with ZN image even though the pathologists are absent 7 . Due to increase in number of TB patient, the diagnosis of the same is highly essential.…”

Section: Introductionmentioning

confidence: 99%

Severity detection and infection level identification of tuberculosis using deep learning

Chithra

Jagatheeswari

2020

Int J Imaging Syst Tech

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Tuberculosis (TB) is a highly infectious disease and is one of the major health problems all over the world. The accurate detection of TB is a major challenge faced by most of the existing methods. This work addresses these issues and developed an effective mechanism for detecting TB using deep learning. Here, the color space transformation is applied for transforming the red green and blue image to LUV space, where L stands for luminance, U and V represent chromaticity values of color images. Then, adaptive thresholding is carried out for image segmentation and various features, like coverage, density, color histogram, area, length, and texture features, are extracted to enable effective classification. After the feature extraction, the size of the features is reduced using principal component analysis. The extracted features are subjected to fractional crow search‐based deep convolutional neural network (FC‐SVNN) for the classification. Then, the image level features, like bacilli count, bacilli area, scattering coefficients and skeleton features are considered to perform severity detection using proposed adaptive fractional crow (AFC)‐deep CNN. Finally, the inflection level is determined using entropy, density and detection percentage. The proposed AFC‐Deep CNN algorithm is designed by modifying FC algorithm using self‐adaptive concept. The proposed AFC‐Deep CNN shows better performance with maximum accuracy value as 0.935.

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Automated tuberculosis screening using Zeihl Neelson image

Cited by 15 publications

References 7 publications

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Segmentation of Tuberculosis Bacilli Using Watershed Transformation and Fuzzy C-Means

Severity detection and infection level identification of tuberculosis using deep learning

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