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Uncertainty assisted robust tuberculosis identification with Bayesian convolutional neural networks

Ul Abideen, Zain; Ghafoor, Mubeen; Munir, Kamran; Saqib, Madeeha; Ullah, Ata; Zia, Tehseen; Tariq, Syed Ali; Ahmed, Ghufran; Zahra, Asma


Zain Ul Abideen

Mubeen Ghafoor

Kamran Munir
Associate Professor in Data Science

Madeeha Saqib

Ata Ullah

Tehseen Zia

Syed Ali Tariq

Ghufran Ahmed

Asma Zahra


Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.

Journal Article Type Article
Publication Date Jan 28, 2020
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 8
Pages 22812-22825
APA6 Citation Zahra, A., Zia, T., Ullah, A., Saqib, M., Ahmed, G., Tariq, S. A., …Munir, K. (2020). Uncertainty assisted robust tuberculosis identification with Bayesian convolutional neural networks. IEEE Access, 8, 22812-22825.
Keywords General Engineering; General Materials Science; General Computer Science
Publisher URL


Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks (1.5 Mb)


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