Federated Learning Model For Breast Cancer Detection Using Mammogram Images

Breast cancer is one of the most common malignancies among women. Early diagnosis plays a critical role in increasing the effectiveness of treatment. Nowadays, artificial intelligence (AI) and deep learning methods are widely used for detecting cancer based on mammogram images. However, due to the privacy and distributed nature of medical data, traditional centralized training approaches face practical limitations. This paper proposes a federated learning approach for detecting breast cancer based on mammogram images. In the proposed method, medical data is stored locally across multiple centers (e.g., hospitals), and each center trains a model locally. Subsequently, the updates from these local models are aggregated on a central server to produce a global model. This approach enables large-scale model training on real clinical data without compromising data privacy. Experiments were conducted on publicly available datasets such as CBIS-DDSM and INbreast. The proposed federated model achieved competitive accuracy and sensitivity compared to conventional centralized models. The results demonstrate that federated learning is a reliable and privacy-preserving alternative for breast cancer detection.

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