Analysis of Tuberculosis Detection Techniques Using Chest X-Rays: A Review

Yousuf, Z.; Akram, Usman; Jameel, Amina; Raza, Wajiha

doi:10.1109/spc.2018.8704137

Cited by 3 publications

(1 citation statement)

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“…A detailed overview of such automated TB detection techniques along with the datasets is presented in ref. [12] highlighting the importance of data volume for opting deep learning over the handcrafted machine learning algorithms.…”

Section: Related Workmentioning

confidence: 99%

A deep learning approach for the classification of TB from NIH CXR dataset

Zaidi

Akram

Jameel

et al. 2021

IET Image Processing

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In this research, a novel customized deep learning model is proposed to detect Tuberculosis (TB) from chest X‐rays (CXR). The model is utilized for three experimentations: (i) classification of CXR image as healthy or TB infected, (ii) sub‐classification of infected images to TB specific manifestations, and (iii) classification of CXR image to thoracic disease manifestations. The National Institute of Health (NIH) CXR is used for experimentation. For the first two experimentations, the subset of the dataset is used containing only 10 TB specific manifestations, whereas, the entire NIH CXR dataset is used for the third experiment. The F1 score for binary classification of TB in experiment 1 is calculated as 0.92 which is higher than the average F1 score of the radiologists. The average accuracy for classifying TB specific manifestations in experiment 2 is recorded as 0.84. Finally, the average accuracy of the thoracic disease classification is recorded as 0.82 in experiment 3. The proposed system outperformed the existing approaches reporting higher AUC for each manifestation. Whereas, to the best of knowledge it is the first such attempt on NIH CXR dataset for TB and TB specific manifestation classification and the proposed system showed promising results.

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Section: Related Workmentioning

confidence: 99%