TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images

• URL: http://arxiv.org/abs/2104.03165v1
• Date: Tue, 6 Apr 2021 14:09:05 GMT
• Title: TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images
• Authors: Alexander Wong, James Ren Hou Lee, Hadi Rahmat-Khah, Ali Sabri, and Amer Alaref
• Abstract summary: Tuberculosis remains a global health problem, and is the leading cause of death from an infectious disease. There has been significant interest in artificial intelligence-based TB screening solutions for use in resource-limited scenarios. We introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening.
• Score: 66.93350009086132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tuberculosis (TB) remains a global health problem, and is the leading cause of death from an infectious disease. A crucial step in the treatment of tuberculosis is screening high risk populations and the early detection of the disease, with chest x-ray (CXR) imaging being the most widely-used imaging modality. As such, there has been significant recent interest in artificial intelligence-based TB screening solutions for use in resource-limited scenarios where there is a lack of trained healthcare workers with expertise in CXR interpretation. Motivated by this pressing need and the recent recommendation by the World Health Organization (WHO) for the use of computer-aided diagnosis of TB, we introduce TB-Net, a self-attention deep convolutional neural network tailored for TB case screening. More specifically, we leveraged machine-driven design exploration to build a highly customized deep neural network architecture with attention condensers. We conducted an explainability-driven performance validation process to validate TB-Net's decision-making behaviour. Experiments using a tuberculosis CXR benchmark dataset showed that the proposed TB-Net is able to achieve accuracy/sensitivity/specificity of 99.86%/100.0%/99.71%. Radiologist validation was conducted on select cases by two board-certified radiologists with over 10 and 19 years of experience, respectively, and showed consistency between radiologist interpretation and critical factors leveraged by TB-Net for TB case detection for the case where radiologists identified anomalies. While not a production-ready solution, we hope that the open-source release of TB-Net as part of the COVID-Net initiative will support researchers, clinicians, and citizen data scientists in advancing this field in the fight against this global public health crisis.

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