Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays

ABSTRACT

Tuberculosis (TB) is a contagious bacterial airborne disease, and is one of the top 10 causes of death worldwide. According to the World Health Organisation, around 1.8 billion people are infected with TB and 1.6 million deaths were reported in 2018. More importantly, 95% of cases and deaths were from developing countries. Yet, TB is a completely curable disease through early diagnosis. To achieve this goal one of the key requirements is efficient utilisation of existing diagnostic technologies, among which chest X-ray is the first line of diagnostic tool used for screening for active TB. The presented deep learning pipeline consists of three different modern deep learning architectures, to generate, segment, and classify lung X-rays. Apart from this, image preprocessing, image augmentation, genetic algorithm based hyper parameter tuning, and model ensembling were used to improve the diagnostic process. We were able to achieve classification accuracy of 97.1% (Youden’s index-0.941, sensitivity of 97.9%, specificity of 96.2%) which is a considerable improvement compared to the existing work in the literature. In our work, we present a highly accurate, automated TB screening system using chest X-rays, which would be helpful especially for low income countries with low access to qualified medical professionals.

KEYWORDS:

• Machine Learning
• automated Tuberculosis Screening
• Chest/Lung X-ray analysis
• Deep Convolutional Generative Adversarial Networks
• segmentation and Classification

Disclosure statement

The authors declare that they have no conflict of interest.

Ethical approval

For this type of study formal consent is not require.

Informed consent

The dataset used in this article is freely available. Both datasets were de-identified by the data providers and were exempted from IRB review at their respective institutions. At, NIH the dataset use and public release were exempted from IRB review by the NIH office of Human Resource Research Projection Programs (No. 5357). Ref: Jaeger, Stefan, Sema Candemir, Sameer Antani, Yì-Xiáng J. Wáng, Pu-Xuan Lu,and George Thoma.”Two public chest X-ray datasets for computer-aided screen-ing of pulmonary diseases.” Quantitative imaging in medicine and surgery. 4.6(2014): 475.

Additional information

Funding

This study was not funded by any grant company or person.

Notes on contributors

Chirath Dasanayaka

Chirath Dasanayaka is a machine learning research engineer currently working at a reputed private company located in Colombo Sri Lanka. He graduated as an electrical and electronic engineer from university of Peradeniya, Sri Lanka in 2019 with B.Sc.(Hons) in Engineering. His research works mainly focuses on medical image processing, deep learning, machine learning, pattern recognition and quantitative finance. 

Maheshi Buddhinee Dissanayake

Maheshi Buddhinee Dissanayake received the B.Sc. (Eng.) degree with First Class (Hons) in electrical and electronic engineering from the University of Peradeniya, Sri Lanka, in 2006, and the Ph.D. in electronic engineering from the University of Surrey, U.K., in 2010. Since 2013, she has been a Senior Lecturer with the Department of Electrical and Electronic Engineering, Faculty of Engineering, University of Peradeniya. Her research interests include error correction codes, channel coding, robust video communication, molecular communication, machine learning, and biomedical image analysis. She has served as an organizing committee member and  TPC Member of many IEEE R10 conferences, such as R10 HTC, ICIIS, and WIECON and as a reviewer in IEEE journals;  IEEE TCOM,  IEEE TMBMC, IEEE ACCESS, IEEE TIP, IEEE Systems.