Clinico-radiological Difference between Primary and Secondary MDR Pulmonary Tuberculosis

Kumar, Deependra; Alok,

doi:10.7860/jcdr/2019/39899.12716

Cited by 2 publications

(2 citation statements)

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“…The most widespread areas of SPT lesions are below: apical and posterior segments of the upper lobe, and dorsal segment of the lower lobe. The CT manifestations are listed as: Exudative patchy shadows of uneven density, miliary shadows, proliferative nodular shadows, tree bud signs, caseous pneumonia, tuberculosis bulbs, cavities, satellite foci, bronchial dissemination, cord strips and calcifications and many other forms (Iliyasu et al 2018, Rai andAlok 2019).…”

Section: Introductionmentioning

confidence: 99%

Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Wang

Govindaraj

Górriz

et al. 2021

J Ambient Intell Human Comput

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We propose a novel graph rank-based average pooling neural network (GRAPNN) to detect secondary pulmonary tuberculosis patients via chest CT imaging. (Methods) First, we propose a novel rank-based pooling neural network (RAPNN) to learn the individual image-level features from chest CT images. Second, we integrate the graph convolutional network (GCN), which learns relation-aware representation among the batch of chest CT images, to RAPNN.Third, we build a novel Graph RAPNN (GRAPNN) model based on the previous integration via k-means clustering and knearest neighbors' algorithm. Besides, an improved data augmentation is utilized to handle overfitting problem. Grad-ACM is used to make this GRAPNN model explainable. (Results) This proposed GRAPNN method is compared with seven stateof-the-art algorithms. The results showed GRAPNN model yields the best performances with a sensitivity of 94.65%, a specificity of 95.12%, a precision of 95.17%, an accuracy of 94.88%, and an F1 score of 94.87%. (Conclusions) Our GRAPNN is superior to other seven state-of-the-art approaches. The explainable mechanism in our method can identify the lesions of important lung parts (tuberculosis cavities and surrounding small lesions) for transparent decision.

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Section: Introductionmentioning

confidence: 99%

Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Wang

Govindaraj

Górriz

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…The most widespread locales of SPT lesions include the apical and posterior segments of the upper lobe and the dorsal segment of the lower lobe [7]. The chest CT (CCT) exhibitions are enumerated as exudative patchy shadows of uneven density, miliary shadows, tree bud signs, proliferative nodular shadows, caseous pneumonia, cavities, tuberculosis bulbs, bronchial dissemination, satellite foci, calcifications and cord strips, and lots of other forms [8,9]. In the past, manual analysis of SPT is tedious and onerous.…”

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confidence: 99%

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

et al. 2021

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Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.

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Clinico-radiological Difference between Primary and Secondary MDR Pulmonary Tuberculosis

Cited by 2 publications

References 0 publications

Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Explainable diagnosis of secondary pulmonary tuberculosis by graph rank-based average pooling neural network

Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder

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