Enhancing Soil-Transmitted Helminth Detection in Microscopic Images Using the Chain Code for Object Feature Extraction

Authors

  • Rio Andika Malik University of Perintis Indonesia
  • Marta Riri Frimadani Postgraduate Program of Andalas University, Indonesia
  • Dwipa Junika Putra Universitas Perintis Indonesia

DOI:

https://doi.org/10.25008/ijadis.v4i2.1305

Keywords:

Soil-Transmitted Helminths, Deep Learning, Image processing, Feature Extraction, Chain code

Abstract

Soil-Transmitted Helminth (STH) infections are a grave global health issue, which involves particularly in countries that are developing with insufficient sanitation and limited access to healthcare. With better intestinal helminth egg detection technology, health facilities in areas with limited resources can identify and treat these infections more promptly. It is necessary to create a strong framework and an effective method to solve this challenge. The outcomes of this study could assist in parasite infection discovery and public health. Chain code-based feature extraction strategy can also be the foundation for the development of comparable approaches for diagnosing various parasitic diseases. Overall, the neural network design used in this study makes the model that is produced a good model that assigns well to never-before-seen data. The significance of image processing technologies in the medical field is shown by this study.

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References

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Published

2023-10-06

How to Cite

Malik, R. A., Frimadani, M. R., & Putra, D. J. (2023). Enhancing Soil-Transmitted Helminth Detection in Microscopic Images Using the Chain Code for Object Feature Extraction. International Journal of Advances in Data and Information Systems, 4(2), 181-190. https://doi.org/10.25008/ijadis.v4i2.1305
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