TY - GEN
T1 - Breast Cancer Malignancy Classification Based on Breast Histopathology Images Using Convolutional Neural Network
AU - Noviandini, Farah
AU - Darmawan, Bunga Mastiti
AU - Agustin, Rizki Wulan
AU - Endarko,
N1 - Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.
PY - 2022/11/10
Y1 - 2022/11/10
N2 - Breast cancer is one of the main causes of women’s death in Indonesia. The prediction of the breast by medical personnel to classified the type of the breast histopathology image (BreakHis) with high accuracy in a short time is needed. This study aims to determine BreakHis’ malignancy classification, including in the benign or malignant class using the CNN (Convolutional Neural Network) algorithm and determine the optimization’s results of the accuracy benign class and malignant class using architectures of MobileNetV2 and ResNet50V2. In this study, 7891 BreakHis datasets are used with 40´, 100´, 200´, and 400´ factors from the Kaggle website. The whole image is resized to 224´224 pixels and used Jupiter with the Python programming language to perform this study. The results showed the highest accuracy in the ResNet50V2 model with accuracy values of 100% for training data, 95.8% for testing, and 97% for validation.
AB - Breast cancer is one of the main causes of women’s death in Indonesia. The prediction of the breast by medical personnel to classified the type of the breast histopathology image (BreakHis) with high accuracy in a short time is needed. This study aims to determine BreakHis’ malignancy classification, including in the benign or malignant class using the CNN (Convolutional Neural Network) algorithm and determine the optimization’s results of the accuracy benign class and malignant class using architectures of MobileNetV2 and ResNet50V2. In this study, 7891 BreakHis datasets are used with 40´, 100´, 200´, and 400´ factors from the Kaggle website. The whole image is resized to 224´224 pixels and used Jupiter with the Python programming language to perform this study. The results showed the highest accuracy in the ResNet50V2 model with accuracy values of 100% for training data, 95.8% for testing, and 97% for validation.
UR - http://www.scopus.com/inward/record.url?scp=85142938296&partnerID=8YFLogxK
U2 - 10.1063/5.0103186
DO - 10.1063/5.0103186
M3 - Conference contribution
AN - SCOPUS:85142938296
T3 - AIP Conference Proceedings
BT - 4th International Conference of Science and Education Science, IConSSE 2021
A2 - Nugroho, Didit Budi
A2 - Setiawan, Andreas
A2 - Wibowo, Nur Aji
A2 - Riyanto, Cucun Alep
A2 - Aminu, November Rianto
PB - American Institute of Physics Inc.
T2 - 4th International Conference of Science and Education Science: Integrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era, IConSSE 2021
Y2 - 7 September 2021 through 8 September 2021
ER -