DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

  • Odi Nurdiawan (1*) STMIK IKMI Cirebon
  • Heliyanti Susana (2) STMIK IKMI Cirebon
  • Ahmad Faqih (3) STMIK IKMI Cirebon

  • (*) Corresponding Author
Keywords: convolutional neural network, hormones, polycystic ovarian syndrome, pregnancy, reproduction

Abstract

Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.

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Published
2024-02-01
How to Cite
[1]
O. Nurdiawan, H. Susana, and A. Faqih, “DEEP LEARNING FOR POLYCYSTIC OVARIAN SYNDROME CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK”, jitk, vol. 9, no. 2, pp. 218-226, Feb. 2024.
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