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A Novel Ensemble Method for PTB Classification in CXRs

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Abstract

Pulmonary tuberculosis (PTB) is a contagious disease that affects the lung region. PTB is a life-threatening disease if it is detected late or left untreated. To perform the initial screening of PTB, the World Health Organization has recommended chest radiograph. Till now, the screening process requires either the patients to come to secondary health centers from rural areas or the radiologists to go the remote locations. This process is rejuvenated with the introduction of computer-aided diagnosis (CAD) systems. CAD systems reduce the need for expert radiologists in the screening process. However, the development and deployment are still in the early phases as new methods are being developed to improve the performance of CAD systems in terms of accuracy, specificity and sensitivity. In this study, a deep learning-based PTB classification system has been presented that achieves the state-of-the-art performance for TB classification. Firstly, a proposed architecture based on the blocks is presented and then it is used to create an ensemble. In the proposed ensemble, two standard architectures namely AlexNet, and ResNet have also been used in addition to the proposed architecture. All the architectures are trained and evaluated on a combined dataset formed using publicly available standard datasets. The proposed ensemble attains the accuracy of 90.00% and area under the curve equal to 0.96, which is better than the performance of the existing methods.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Punjab Engineering College (Deemed to be University), Chandigarh, India

    Rahul Hooda & Sanjeev Sofat

  2. UIET, Panjab University, Chandigarh, India

    Ajay Mittal

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  1. Rahul Hooda
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  2. Ajay Mittal
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  3. Sanjeev Sofat
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Correspondence to Ajay Mittal.

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Hooda, R., Mittal, A. & Sofat, S. A Novel Ensemble Method for PTB Classification in CXRs. Wireless Pers Commun 112, 809–826 (2020). https://doi.org/10.1007/s11277-020-07075-x

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