Breast Cancer Malignancy Classification Based on Breast Histopathology Images Using Convolutional Neural Network

Farah Noviandini, Bunga Mastiti Darmawan, Rizki Wulan Agustin, Endarko*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication4th International Conference of Science and Education Science, IConSSE 2021
Subtitle of host publicationIntegrating Rapid Technology and Whole Person Education in Science and Science Education to Encounter the New Normal Era
EditorsDidit Budi Nugroho, Andreas Setiawan, Nur Aji Wibowo, Cucun Alep Riyanto, November Rianto Aminu
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735442597
DOIs
Publication statusPublished - 10 Nov 2022
Event4th 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 - Salatiga, Virtual, Indonesia
Duration: 7 Sept 20218 Sept 2021

Publication series

NameAIP Conference Proceedings
Volume2542
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th 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
Country/TerritoryIndonesia
CitySalatiga, Virtual
Period7/09/218/09/21

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