Green AI-Enhanced Deep Learning Model for Breast Cancer Detection and Classification in Mammography Images: BC-Net-512

Authors

Nesma Abd El-Mawla, Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, New Mansoura University, New Mansoura, EgyptFollow
Mohamed A. Berbar, Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt
Nawal A. El-Fishawy, Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt
Mohamed A. El-Rashidy, Department of Computer Science and Engineering, Faculty of Electronic Engineering, Menoufia University, Egypt - Department of Computer, Arab East Colleges, Kingdom of Saudi Arabia

Corresponding Author

Nesma Abd El-Mawla

Subject Area

Computer Science

Article Type

Original Study

Abstract

This study champions a sustainable approach for developing a Deep Learning (DL) model for medical image analysis, specifically focusing on breast cancer (BC) detection in mammograms. By prioritizing low-computing algorithms to achieve high diagnostic accuracy while minimizing the model's environmental footprint, that aligns with the principles of Green AI. In this paper, an innovative architecture called BC-Net-512 was constructed for the classification of BC mammography. It is composed of lightweight Convolutional Neural Network (CNN) blocks for texture, density, and structure feature extraction and detection, a thin, fully connected layer for learning complex patterns and correlations in the extracted features, and a dropout layer for mitigating overfitting concerns. Five CNN architectures are also proposed to assess the structural effectiveness of the BC-Net-512 model in terms of computational complexity and classification accuracy. The proposed BC-Net-512 model demonstrated peak accuracy and significantly reduced computational complexity, surpassing DL methods and other state-of-the-art algorithms, meeting the Green AI requirements for efficient and sustainable AI models. It demonstrates promising results for accurate BC classification tasks. Due to experimental investigations, BC-Net-512 outperformed other related works on the two benchmark datasets, achieving 93.16% classification accuracy in the DDSM dataset and 100% in the INbreast dataset, surpassing state-of-the-art methods by 2.0% and 0.3%, respectively. Moreover, BC-Net-512 demonstrated a remarkable 98.80% decrease in computing complexity, underscoring its computational efficiency.

Keywords

Deep learning, Breast cancer, Healthcare systems, Green AI; Convolutional Neural Network, Tumor

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Abd El-Mawla, Nesma; Berbar, Mohamed A.; El-Fishawy, Nawal A.; and El-Rashidy, Mohamed A. (2025) "Green AI-Enhanced Deep Learning Model for Breast Cancer Detection and Classification in Mammography Images: BC-Net-512," Mansoura Engineering Journal: Vol. 50 : Iss. 5 , Article 12.
Available at: https://doi.org/10.58491/2735-4202.3378