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All Journal Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Jurnal Pilar Nusa Mandiri International Journal of Research in Community Services International Journal of Quantitative Research and Modeling Jurnal Pengabdian Masyarakat Tapis Berseri SisInfo : Jurnal Sistem Informasi dan Informatika INTERNAL (Information System Journal) Bulletin of Intelligent Machines and Algorithms
Nursyanti, Reni
Fakultas Teknologi Dan Informatika, Universitas Informatika Dan Bisnis Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Energy Engineering Environmental Science Physics Social Sciences Other

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Journal : bulletin of intelligent machines and algorithms

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Enhancing Breast Cancer Diagnosis with Ensemble Learning: Leveraging Convolutional Neural Networks and Pretrained Models through Averaged Predictions Elia Setiana; Reni Nursyanti; Nur Alamsyah; Nayla Nurul Azkiya
Bulletin of Intelligent Machines and Algorithms Vol. 1 No. 4 (2026): BIMA May 2026 Issue
Publisher : Maheswari Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.65780/bima.v1i4.26

Abstract

Breast cancer continues to be a serious health issue at the global level, and early detection can significantly improve patient outcomes. This research uses imaging techniques to examine the design of an improved classification model in breast cancer detection. This project uses deep learning approaches through Convolutional Neural Networks (CNN) and ensemble learning models to potentially improve classification accuracy. To further enhance performance while controlling for class imbalance and overfitting, we leverage several models, such as ResNet18 and VGG16, with data augmentation and pre-trained models. Our methods included standard preprocessing of medical images, splitting datasets into training, testing and validation sets, and training each model with the Adam optimizer. Performance measurement included accuracy, precision, recall, and F1 score metrics. Overall, prototypes recently created displayed clear advantages based on finding results achieved through an ensemble method, which demonstrated improved model stability and reduced significant misclassification errors, and model accuracy reached 0.96. This research is crucial while developing strong deep-learning models to aid in breast cancer detection, ultimately allowing us to set a base for developing better diagnostic inference systems in medical-based applications. These systems may help improve early detection and overall patient care.
Co-Authors Akbar, Imannudin Aldi Rohman Anggi Dewi Nurcahyani Asep Ramdani Budiman Cecep Kurniawan Chairul Habibi Gagas Langgeng Dwi Prastyo Graha Prakarsa Luthfi Fadle Rynaldy Muhammad Syarif Hartawan Nasution, Vani Maharani Nayla Nurul Azkiya Nur Alamsyah Nur Alamsyah R. Yadi Rakhman R. Yadi Rakhman Alamsyah R.Yadi Rakhman Alamsyah Resvina Alya Putri Sardjono Sardjono Setiana, Elia Surya Perdana Valencia Claudia Jennifer Kaunang Venia R Danestiara