Title: CAD-based automatic detection of tuberculosis in chest radiography using hybrid method

Authors: M. Mercy Theresa; A. Jesudoss; P. Pattunnarajam; Sudha Rajesh; Jaanaa Rubavathy; A. Raja

Addresses: Department of Data Science and Business Systems, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India ' Department of Computer Science and Engineering, School of Computing, Sathyabama Institute of Science and Technology, Chennai, India ' Department of Electronics and Communication Engineering, Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, India ' Department of Computational Intelligence, School of Computing, College of Engineering and Technology, SRMIST, Kattankulathur, Chennai, India ' Department of Electrical and Electronics Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India ' Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India

Abstract: Automated processes are essential in medical imaging to identify anomalies. This study uses chest radiography (CXR) for CAD analysis, which is indicated for about 90% of TB patients. Even when it is cost effective, certain reasons are difficult to pinpoint. Using a deformable active contour model, the first phase involves inputting CXR lung field segmentation and identifying highlights within the segmented lung region, along with TB detection calculations. The algorithm's segmentation output is evaluated using two parameters. Moving on to the second phase, features are extracted and optimised using a hybrid multiresolution approach. Various transform coefficients were statistically analysed to obtain a feature collection. The final stage is to classify lung anomalies using MSVM and KNN for three publicly available datasets. The classification performance of the JSRT, Montgomery, and Shenzhen datasets is assessed. The recommended method identifies pulmonary TB 96.5% of the time.

Keywords: computer-aided diagnosis; CAD; chest X-ray; CXR; lung segmentation; novel active contour model; hybrid multiresolution approach; feature extraction; classification.

DOI: 10.1504/IJESMS.2023.133940

International Journal of Engineering Systems Modelling and Simulation, 2023 Vol.14 No.4, pp.179 - 185

Received: 31 Jul 2021
Accepted: 17 Nov 2021
Published online: 06 Oct 2023 *

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