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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Bulletin of Electrical Engineering and Informatics Abdimas JEJAK Jurnal Transformatika
Dian Tri Wiyanti
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Arts Humanities Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Social Sciences

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Journal : Bulletin of Electrical Engineering and Informatics

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Integration of convolutional neural network and extreme gradient boosting for breast cancer detection Endang Sugiharti; Riza Arifudin; Dian Tri Wiyanti; Arief Broto Susilo
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3562

Abstract

With the most recent advances in technology, computer programming has reached the capabilities of human brain to decide things for almost all healthcare systems. The implementation of Convolutional Neural Network (CNN) and Extreme Gradient Boosting (XGBoost) is expected to improve the accurateness of breast cancer detection. The aims of this research were to; i) determine the stages of CNN-XGBoost integration in diagnosis of breast cancer and ii) calculate the accuracy of the CNN-XGBoost integration in breast cancer detection. By combining transfer learning and data augmentation, CNN with XGBoost as a classifier was used. After acquiring accuracy results through transfer learning, this reasearch connects the final layer to the XGBoost classifier. Furthermore, the interface design for the evaluation process was established using the Python programming language and the Django platform. The results: i) the stages of CNN-XGBoost integration on histopathology images for breast cancer detection were discovered. ii) Achieved a higher level of accuracy as a result of the CNN-XGBoost integration for breast cancer detection. In conclusion, breast cancer detection was revealed through the integration of CNN-XGBoost through histopathological images. The combination of CNN and XGBoost can enhance the accuracy of breast cancer detection.
Co-Authors Agus Dwi Nugroho, Agus Dwi Arief Broto Susilo Avi Budi Setiawan Aziz Ahmadi Bernadus Very Christioko David Mubarok Endang Sugiharti, Endang Maslikhatun, Anjani MHD. Reza M.I. Pulungan Mochammad Yusuf, Mochammad Muhammad Zuhair Zahid, Muhammad Zuhair Prasetyo Ari Bowo, Prasetyo Ari Riza Arifudin