Detection of Circulatory Diseases Through Fingernails Using Artificial Neural Network

Authors

  • Lean Karlo Tolentino Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.
  • Renz Marion Aragon Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.
  • Winnie Rose Tibayan Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.
  • Angelie Alvisor Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.
  • Pauline Grace Palisoc Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.
  • Geralyn Terte Electronics Engineering Department, College of Engineering, Technological University of the Philippines, Manila 1000, Philippines.

Keywords:

Artificial Neural Network, Circulatory Disease, Fingernails, Image Processing,

Abstract

This study focuses on detection of circulatory diseases such as Coronary Occlusion, Congestive Heart Failure, and Congenital Heart Disease by analyzing fingernails. It used an image processing system which includes image segmentation, color threshold, and shape analysis. The fingernail database used are classified using Artificial Neural Network (ANN). The proposed detection system diagnosed 6 patients having the said diseases (3 Congenital Heart Failure, 2 Congenital Heart Disease, and 1 Coronary Occlusion). It was matched with all the findings and diagnosis of all the attending specialists. With this, it was 100% successful in detecting circulatory diseases.

Downloads

Download data is not yet available.

Downloads

Published

2018-01-29

How to Cite

Tolentino, L. K., Aragon, R. M., Tibayan, W. R., Alvisor, A., Palisoc, P. G., & Terte, G. (2018). Detection of Circulatory Diseases Through Fingernails Using Artificial Neural Network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(1-4), 181–188. Retrieved from https://jtec.utem.edu.my/jtec/article/view/3614