Klasifikasi Kanker Payudara Berdasarkan Gambar Histopatologi Menggunakan Metode Convolutional Neural Network Dengan Arsitektur VGG-16 - Nandasari, Dayang; Pratama, Irfan (Building of Informatics, Technology and Science (BITS), 2025)
Breast cancer is one of the deadliest diseases with a high prevalence worldwide, especially in women. Breast cancer is the third leading cause of death in Indonesia. Based on Globocan Center data, there will be approximately 408,661 new cases and nearly 242,099 deaths in Indonesia by 2022. Early detection through histopathology images is very important to increase the patient's chances of recovery. However, the diagnosis process carried out manually by pathologists is quite time consuming and affects subjectivity. This study aims to develop a histopathology image-based breast cancer classification system using VGG-16. The dataset to be used consists of histopathology images that are grouped into 2 classes, namely benign and malignant. The data went through several preprocessing stages, including splitting and augmentation, to improve data quality. Test results show that this model achieves 91% accuracy, along with high precision, recall, and F1-scores on the test data. The performance of this model compares favorably with ensemble architectures such as, MobileNet, MobileNetV2. These findings indicate that the proposed approach can be an effective solution as a histopathology image-based breast cancer diagnosis tool.