Screening of Tuberculosis Using Artificial Neural Network

Authors

  • A. Anand Kumar Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India
  • T. Mani Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India
  • S. Gokulnath Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India
  • S. K. Kabilesh Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India
  • K. Dinakaran Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India
  • A. Stephen Sagayaraj Assistant Professor, Department of Electronics and Communication Engineering, Jai Shriram Engineering College, Tirupur, India

DOI:

https://doi.org/10.47607/ijresm.2020.392

Keywords:

Computer-helped location and finding, Division, Lung design acknowledgment and order, Tuberculosis (TB), X-beam imaging

Abstract

Tuberculosis is an infectious bacterial disease that most commonly affects the lungs. This paper reviews, screening of tuberculosis in chest radiograph images using an artificial neural network (ANN). Implementing image processing techniques having segmentation, feature extraction from chest radiographs, at that point building up a fake neural organization for programmed characterization dependent on back proliferation calculation to group tuberculosis accurately. The performance was evaluated using SVM and ANN classifiers regarding exactness, review, and precision. The trial results Confirm the effectiveness of the proposed strategy that gives great Classification proficiency.

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Published

03-12-2020

Issue

Section

Articles

How to Cite

[1]
A. A. Kumar, T. Mani, S. Gokulnath, S. K. Kabilesh, K. Dinakaran, and A. S. Sagayaraj, “Screening of Tuberculosis Using Artificial Neural Network”, IJRESM, vol. 3, no. 11, pp. 145–149, Dec. 2020, doi: 10.47607/ijresm.2020.392.

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