A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter

Pratikno, Heri, Ibrahim, Mohd Zamri, and Jusak, (2022) A novel fern-like lines detection using a hybrid of pre-trained convolutional neural network model and Frangi filter. Telkomnika, 20 (3). pp. 607-620.

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution Share Alike.

Download (885kB) | Preview
View at Publisher Website: https://doi.org/10.12928/telkomnika.v20i...
 
14


Abstract

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.

Item ID: 75491
Item Type: Article (Research - C1)
ISSN: 2302-4046
Keywords: Deep learning, Fern-like lines, Frangi filter, ResNet34, Salivary ferning
Copyright Information: This is an open access article under the CC BY-SA license
Date Deposited: 13 Sep 2022 01:06
FoR Codes: 40 ENGINEERING > 4003 Biomedical engineering > 400304 Biomedical imaging @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 60%
22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220404 Computer systems @ 40%
Downloads: Total: 14
Last 12 Months: 14
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page