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Journal of Modern Mechanical Engineering and Technology

Articles

Vol. 7 (2020)

Enhancement of Tuberculosis Detection Using Ensemble Classifier with Quadtree Method: A Preliminary Study

DOI
https://doi.org/10.31875/2409-9848.2020.07.1
Submitted
June 25, 2020
Published
2020-06-25

Abstract

Tuberculosis is an infectious disease caused by a bacillus called Mycobacterium tuberculosis. It can lead to death in untreated and inappropriately treated patients. An early diagnosis of the disease not only improves treatment success but also reduces death rates. Lung region is the most affected part of Tuberculosis and the process of medical image classification is still carried out manually using the knowledge of the physician or radiologist, which leads to inaccurate and slow process of TB identification. Therefore, this study proposed to enhance tuberculosis detection using a different combination of machine learning and image processing methods on the image dataset.

References

  1. Mathews A, A VR. Detection of Tuberculosis Using Chest XRays. International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE) 2015; 4(6).
  2. Antony B, NB K. Lung Tuberculosis Detection Using X-Ray Images. International Journal of Applied Engineering Research 2017; ISSN, 15196-15201.
  3. Melendez J, Ginneken BV, Maduskar P, Philipsen RH, Reither K, Breuninger M, Sánchez CI. A Novel MultipleInstance Learning-Based Approach to Computer-Aided Detection of Tuberculosis on Chest X-Rays. IEEE Transactions on Medical Imaging 2015; 34(1): 179-192. https://doi.org/10.1109/TMI.2014.2350539
  4. Rusdah, Winarko, E., Wardoyo, R. Preliminary diagnosis of pulmonary tuberculosis using ensemble method. International Conference on Data and Software Engineering (ICoDSE) – IEEE 2015. https://doi.org/10.1109/ICODSE.2015.7436993
  5. Lee J, Lee S-K, Yang S-I. An Ensemble Method of CNN Models for Object Detection. 2018 International Conference on Information and Communication Technology Convergence (ICTC) 2018. https://doi.org/10.1109/ICTC.2018.8539396
  6. Li Y, Meng MQ-H, Li S, Chen W, You Z, Guo Q, Liang H. A quadtree based neural network approach to real-time path planning. 2007 IEEE International Conference on Robotics and Biomimetics (ROBIO) 2007; 1350-1354.
  7. He Y, Xu C, Khanna N, Boushey CJ, Delp EJ. Food image analysis: Segmentation, identification and weight estimation. 2013 IEEE International Conference on Multimedia and Expo (ICME) 2013; 2744-2748. https://doi.org/10.1109/ICME.2013.6607548
  8. Treebupachatsakul T, Poomrittigul S. Bacteria Classification using Image Processing and Deep learning. 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC) 2019; 1-3. https://doi.org/10.1109/ITC-CSCC.2019.8793320
  9. Paras Lakhani BS. Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks. THORACIC IMAGING: Deep Learning at Chest Radiography 2017. https://doi.org/10.1148/radiol.2017162326
  10. Liu C, Cao Y, Alcantara M, Liu B, Brunette M, Peinado J, Curioso W. Detecting tuberculosis in chest X-ray images using convolutional neural network. IEEE International Conference on Image Processing (ICIP) 2017. https://doi.org/10.1109/ICIP.2017.8296695
  11. Marquez GR, Escalante HJ, Sucar LE. Simplified Quadtree Image Segmentation for Image Annotation. Chapter in Proceedings of the 2010 Automatic Image Annotation and Retrieval Workshop 2010; 719: 24-34.
  12. Deivanayagampillai N. Statistical based image comparision by quad tree segmentation. International Journal of Research in Engineering and Applied Sciences (IJREAS) 2017; 7(1): 57-63.