Pratikno, Heri, Ibrahim, Mohd Zamri, 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.

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Abstract

There are several methods to predict a woman's ovulation time, including using a calendar system, basal bodytemperature, 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.

Item ID:	83505
Item Type:	Article (Research - C1)
ISSN:	2302-9285
Copyright Information:	This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Date Deposited:	03 Dec 2024 03:47
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing @ 50% 40 ENGINEERING > 4003 Biomedical engineering > 400399 Biomedical engineering not elsewhere classified @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 60% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220402 Applied computing @ 40%
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