Implementation of a Hybrid MobileNetV2 Model for Early Detection of Breast Cancer Based on Mammography Images from RSUP Dr. M. Djamil Padang

Lily Aulia; Yuhandri; Sumijan

doi:10.35134/komtekinfo.v13i3.701

Authors

Lily Aulia Informatics Engineering
Yuhandri Universitas Putra Indonesia "YPTK" Padang
Sumijan Universitas Putra Indonesia "YPTK" Padang

DOI:

https://doi.org/10.35134/komtekinfo.v13i3.701

Keywords:

breast cancer, hybrid deep learning, MobileNetV2, XGBoost, mammography

Abstract

Breast cancer poses a significant health risk to women; however, early detection through mammographic imaging continues to face challenges in achieving high diagnostic accuracy using conventional diagnostic methods. Deep learning offers a solution for improving the accuracy of medical image classification; however, a single model remains limited, so a hybrid approach combining two methods is needed to achieve optimal performance. This study aims to develop and evaluate a Hybrid Deep Learning model that combines MobileNetV2 and XGBoost to detect and classify breast cancer based on mammography images. The research method begins with image preprocessing consisting of resizing, normalization, and augmentation, after which the dataset is split into training and testing data with an 80:20 ratio. MobileNetV2 is used as a feature extractor, producing a 1,280-dimensional feature vector, which is then classified by XGBoost into three classes: normal, benign, and malignant. The dataset consists of 224x224-pixel breast cancer mammography images sourced from RSUP Dr. M. Djamil Padang and the Kaggle platform. The test results show that the Hybrid MobileNetV2 and XGBoost model achieved an accuracy of 75%, a weighted precision of 77%, a weighted recall of 75%, and a weighted F1-score of 74%. The model performed best on the normal class, while the benign class experienced misclassification due to visual similarity with other classes. This study is expected to serve as a foundation for developing an accurate and efficient digital image-based breast cancer detection system to support medical personnel in clinical decision-making.

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