KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN METODE CONVULATIONAL NEURAL NETWORK DENGAN ARSITEKTUR RESNET-50 DAN VGG-16

IDAWATI, IDAWATI and Rini, Dian Palupi and Primanita, Anggina (2024) KLASIFIKASI KANKER PAYUDARA MENGGUNAKAN METODE CONVULATIONAL NEURAL NETWORK DENGAN ARSITEKTUR RESNET-50 DAN VGG-16. Masters thesis, Sriwijaya University.

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Abstract

Breast cancer is one of the leading causes of death among women worldwide. Early detection of breast cancer is crucial to increase the chances of recovery. This study aims to develop a breast cancer classification system using the Convolutional Neural Network (CNN) method with ResNet-50 and VGG-16 architectures. The data used in this study are breast ultrasound images obtained from a public dataset. The CNN model is trained and tested to classify breast images into three classes: normal, benign, and malignant. This study employs ResNet-50 and VGG-16 architectures to evaluate the model's performance in breast cancer classification. The evaluation results show that the ResNet-50 model achieved an accuracy of 81.5% in the testing phase, while the VGG-16 model achieved an accuracy of 88%. Both models are compared based on evaluation metrics such as accuracy, precision, recall, F1-score, and ROC curve. This study makes a significant contribution to improving early breast cancer detection through the application of advanced CNN architectures. It is hoped that the results of this study can help in more effectively identifying breast cancer cases and support efforts in prevention and treatment of the disease.

Item Type:	Thesis (Masters)
Uncontrolled Keywords:	Kanker Payudara
Subjects:	T Technology > TR Photography > TR624-835 Applied photography Including artistic, commercial, medical photography, photocopying processes
Divisions:	09-Faculty of Computer Science > 55101-Informatics (S2)
Depositing User:	Idawati Idawati
Date Deposited:	17 Jul 2024 04:40
Last Modified:	17 Jul 2024 04:40
URI:	http://repository.unsri.ac.id/id/eprint/151325

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