PREDIKSI POLYCYSTIC OVARY SYNDROME (PCOS) PADA CITRA ULTRASONOGRAFI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN)

SARASWATI, PUTRI ANGEL and Rini, Dian Palupi and Rizqie, M. Qurhanul (2025) PREDIKSI POLYCYSTIC OVARY SYNDROME (PCOS) PADA CITRA ULTRASONOGRAFI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (CNN). Undergraduate thesis, Sriwijaya University.

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Abstract

Polycystic Ovary Syndrome (PCOS) is a common endocrine system disorder affecting women of reproductive age and a leading cause of infertility. Early detection is crucial to prevent long-term complications, and ovarian ultrasonography images have emerged as an effective non-invasive tool in supporting PCOS diagnosis. This study aims to compare and optimize the performance of Convolutional Neural Network (CNN) models in classifying ovarian ultrasonography images for PCOS prediction. Three CNN architectures (DenseNet201, VGG19, and InceptionV3) were tested with various configurations, involving two types of optimizers (Adam and SGD) and four learning rate values (0.01, 0.001, 0.0001, and 0.00001). The results demonstrate that the optimal configuration is the InceptionV3 architecture with the Adam optimizer and a learning rate of 0.001, achieving the highest accuracy and F1-score of 99.78%. The Adam optimizer proved superior to SGD, and the precise determination of the learning rate was crucial in achieving peak model performance. These findings indicate that the selection of architecture, optimizer, and learning rate are key factors determining the success of PCOS prediction in ultrasonography images.

Item Type: Thesis (Undergraduate)
Uncontrolled Keywords: Prediction, Polycystic Ovary Syndrome (PCOS), Convolutional Neural Network (CNN), DenseNet201, VGG19, InceptionV3
Subjects: Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions: 09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User: Putri Angel Saraswati
Date Deposited: 22 Jul 2025 03:00
Last Modified: 22 Jul 2025 03:00
URI: http://repository.unsri.ac.id/id/eprint/179577

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