Detection of tuberculosis bacilli from microscopic sputum smear images

Sugirtha, G. Evangelin; Murugesan, Gowtham Krishnan

doi:10.1109/icbsii.2017.8082271

Cited by 6 publications

(1 citation statement)

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“…A notable advantage of this ML model is that it allows for visually determining the conditions for the input variables and the leaves. Random forest (RF) is a special DT model, in which more tree structures are analyzed and tested ( 53 , 54 ). Then, the best configuration of trees is selected for the classification or regression, according to a sample from the data set and avoiding model overfitting.…”

Section: Methodsmentioning

confidence: 99%

Machine learning in the loop for tuberculosis diagnosis support

Orjuela-Cañón

Jutinico

Awad³

et al. 2022

Front. Public Health

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The use of machine learning (ML) for diagnosis support has advanced in the field of health. In the present paper, the results of studying ML techniques in a tuberculosis diagnosis loop in a scenario of limited resources are presented. Data are analyzed using a tuberculosis (TB) therapy program at a health institution in a main city of a developing country using five ML models. Logistic regression, classification trees, random forest, support vector machines, and artificial neural networks are trained under physician supervision following physicians' typical daily work. The models are trained on seven main variables collected when patients arrive at the facility. Additionally, the variables applied to train the models are analyzed, and the models' advantages and limitations are discussed in the context of the automated ML techniques. The results show that artificial neural networks obtain the best results in terms of accuracy, sensitivity, and area under the receiver operating curve. These results represent an improvement over smear microscopy, which is commonly used techniques to detect TB for special cases. Findings demonstrate that ML in the TB diagnosis loop can be reinforced with available data to serve as an alternative diagnosis tool based on data processing in places where the health infrastructure is limited.

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Section: Methodsmentioning

confidence: 99%