Detection of Tuberculosis Bacilli in Tissue Slide Images using HMLP Network Trained by Extreme Learning Machine

Authors

  • M. K. Osman Universiti Teknologi MARA
  • M. Y. Mashor Universiti Malaysia Perlis
  • H. Jaafar Universiti Sains Malaysia

DOI:

https://doi.org/10.5755/j01.eee.120.4.1456

Abstract

This paper proposes an automated detection of tuberculosis bacilli in Ziehl-Neelsen-stained tissue slides using image processing and neural network. Image segmentation using CY-based colour filter and k-mean clustering procedure is used to separate objects of interest from the background. A number of geometrical features are then extracted from the segmented images. A recent training algorithm called Extreme Learning Machine (ELM) is modified to train a hybrid multilayered perceptron network (HMLP) for the classification task. The results indicate that the performance of HMLP-ELM network is comparable to the previously proposed methods and offers a fast training time with no designing parameter required. Ill. 6, bibl. 15, tabl. 1 (in English; abstracts in English and Lithuanian).

DOI: http://dx.doi.org/10.5755/j01.eee.120.4.1456

Downloads

Published

2012-04-03

How to Cite

Osman, M. K., Mashor, M. Y., & Jaafar, H. (2012). Detection of Tuberculosis Bacilli in Tissue Slide Images using HMLP Network Trained by Extreme Learning Machine. Elektronika Ir Elektrotechnika, 120(4), 69-74. https://doi.org/10.5755/j01.eee.120.4.1456

Issue

Vol. 120 No. 4 (2012)

Section

AUTOMATION, ROBOTICS

License

The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.

By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.