TELKOMNIKA Telecommunication, Computing, Electronics and Control
A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter

TELKOMNIKA Telecommunication, Computing, Electronics and Control
A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter

Deep learning, Fern-like lines, Frangi filter, ResNet34, Salivary ferning

Full ferning is the peak of the formation of a salt crystallization line pattern shaped like a fern tree in a woman’s saliva at the time of ovulation. The main problem in this study is how to detect the shape of the salivary ferning line patterns that are transparent, irregular and the surface lighting is uneven. This study aims to detect transparent and irregular lines on the salivary ferning surface using a comparison of 15 pre-trained convolutional neural network models. To detect fern-like lines on transparent and irregular layers, a pre-processing stage using the Frangi filter is required. The pre-trained convolutional neural network model is a promising framework with high precision and accuracy for detecting fern-like lines in salivary ferning. The results of this study using the fixed learning rate model ResNet50 showed the best performance with an error rate of 4.37% and an accuracy of 95.63%. Meanwhile, in implementing the automatic learning rate, ResNet18 achieved the best results with an error rate of 1.99% and an accuracy of 98.01%. The results of visual detection of fern-like lines in salivary ferning using a patch size of 34×34 pixels indicate that the ResNet34 model gave the best appearance.

Heri Pratikno, Mohd Zamri Ibrahim, Jusak

DOI: 10.12928/TELKOMNIKA.v20i3.23319

Universitas Ahmad Dahlan

June 2022

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

PDF

English

Text

TELKOMNIKA Telecommunication, Computing, Electronics and Control