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A novel women's ovulation prediction through salivary ferning using the box counting and deep learning

There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction kit, and OvuScope. This is the first study to predict the time of ovulation in women by calculating the results of detecting the fractal shape of the fu...

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Main Authors: Pratikno, Heri, Mohd Zamri, Ibrahim, Jusak, Jusak
Format: Article
Language:English
Published: IAES 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41980/1/A%20novel%20women%27s%20ovulation%20prediction%20through%20salivary%20ferning%20using%20the%20box%20counting%20and%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/41980/
https://doi.org/10.11591/eei.v13i2.5847
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spelling my.ump.umpir.419802024-07-19T03:25:41Z http://umpir.ump.edu.my/id/eprint/41980/ A novel women's ovulation prediction through salivary ferning using the box counting and deep learning Pratikno, Heri Mohd Zamri, Ibrahim Jusak, Jusak TK Electrical engineering. Electronics Nuclear engineering There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction kit, and OvuScope. This is the first study to predict the time of ovulation in women by calculating the results of detecting the fractal shape of the full ferning (FF) line pattern in salivary using pixel counting, box counting, and deep learning for computer vision methods. The peak of a woman's ovulation every month in her menstrual cycle occurs when the number of ferning lines is the most numerous or dense, and this condition is called FF. In this study, the computational results based on the visualization of the fractal shape of the salivary ferning line pattern from the pixel-counting method have an accuracy of 80%, while the fractal dimensions achieved by the box-counting are 1.474. On the other hand, using the deep learning image classification, we obtain the highest accuracy of 100% with a precision value of 1.00, recall of 1.00, and F1-score 1.00 on the pre-trained network model ResNet-18. Furthermore, visualization of the ResNet-34 model results in the highest number of patches, i.e., 586 patches (equal to 36,352 pixels), by applying fern-like lines pattern detection with windows size 8x8 pixels. IAES 2024-04 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/41980/1/A%20novel%20women%27s%20ovulation%20prediction%20through%20salivary%20ferning%20using%20the%20box%20counting%20and%20deep%20learning.pdf Pratikno, Heri and Mohd Zamri, Ibrahim and Jusak, Jusak (2024) A novel women's ovulation prediction through salivary ferning using the box counting and deep learning. Bulletin of Electrical Engineering and Informatics, 13 (2). pp. 996-1006. ISSN 2089-3191 (Print); 2302-9285 (Online). (Published) https://doi.org/10.11591/eei.v13i2.5847 10.11591/eei.v13i2.5847
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Pratikno, Heri
Mohd Zamri, Ibrahim
Jusak, Jusak
A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
description There are several methods to predict a woman's ovulation time, including using a calendar system, basal body temperature, ovulation prediction kit, and OvuScope. This is the first study to predict the time of ovulation in women by calculating the results of detecting the fractal shape of the full ferning (FF) line pattern in salivary using pixel counting, box counting, and deep learning for computer vision methods. The peak of a woman's ovulation every month in her menstrual cycle occurs when the number of ferning lines is the most numerous or dense, and this condition is called FF. In this study, the computational results based on the visualization of the fractal shape of the salivary ferning line pattern from the pixel-counting method have an accuracy of 80%, while the fractal dimensions achieved by the box-counting are 1.474. On the other hand, using the deep learning image classification, we obtain the highest accuracy of 100% with a precision value of 1.00, recall of 1.00, and F1-score 1.00 on the pre-trained network model ResNet-18. Furthermore, visualization of the ResNet-34 model results in the highest number of patches, i.e., 586 patches (equal to 36,352 pixels), by applying fern-like lines pattern detection with windows size 8x8 pixels.
format Article
author Pratikno, Heri
Mohd Zamri, Ibrahim
Jusak, Jusak
author_facet Pratikno, Heri
Mohd Zamri, Ibrahim
Jusak, Jusak
author_sort Pratikno, Heri
title A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
title_short A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
title_full A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
title_fullStr A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
title_full_unstemmed A novel women's ovulation prediction through salivary ferning using the box counting and deep learning
title_sort novel women's ovulation prediction through salivary ferning using the box counting and deep learning
publisher IAES
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41980/1/A%20novel%20women%27s%20ovulation%20prediction%20through%20salivary%20ferning%20using%20the%20box%20counting%20and%20deep%20learning.pdf
http://umpir.ump.edu.my/id/eprint/41980/
https://doi.org/10.11591/eei.v13i2.5847
_version_ 1822924517779963904
score 13.232414

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