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

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

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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.

Availability
Inventory Code Barcode Call Number Location Status
2407004404T151325T1513252024Central Library (Reference)Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T1513252024
Publisher
Indralaya : Program Magister Ilmu Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya.,
Collation
xv, 69 hlm.; ilus.; 29 cm
Language
Indonesia
ISBN/ISSN
-
Classification
006.310 7
Content Type
Text
Media Type
unmediated
Carrier Type
-
Edition
-
Subject(s)
Kanker payudara
Program Magister Ilmu Komputer
Specific Detail Info
-
Statement of Responsibility
Other version/related
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