Breast Cancer Detection Using CNNs on Mammogram Images: A Dataset-Level Comparison of CBIS-DDSM, INbreast, and MIAS
Keywords:
Breast Cancer Detection, Mammogram Classification, CNN, Deep Learning, Medical Image Analysis, Benign vs Malignant, Grad-CAM Explainability, Computer-Aided Diagnosis (CAD)Abstract
This study presents a CNN-based deep learning model for the automated diagnosis and classification of breast cancer using only mammographic images. Mammograms are a primary tool in breast cancer screening due to their accessibility and cost-effectiveness. The proposed model performs end-to-end learning from raw mammographic inputs through convolutional layers to predict benign or malignant conditions. Preprocessing techniques such as contrast enhancement and noise removal are applied to improve image quality. The model is trained and validated using publicly available mammography datasets (e.g., CBIS-DDSM), achieving high diagnostic performance in terms of accuracy, sensitivity, and specificity. Explainability techniques like Grad-CAM are incorporated to visualize regions of interest contributing to the diagnosis.
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Alom, M. Z., Yakopcic, C., Nasrin, M. S., Taha, T. M., & Asari, V. K. (2020). Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network. Journal of Digital Imaging, 33(3), 748–759.
Awan, M. J., Alzahrani, F. A., Mirza, A., & Nawaz, M. (2021). Efficient detection of breast cancer using improved convolutional neural networks. Journal of Healthcare Engineering, 2021, 1–9.
Dhungel, N., Carneiro, G., & Bradley, A. P. (2021). Deep learning and structured prediction for the segmentation of mass in mammograms. Medical Image Analysis, 53, 197–207.
Murtaza, G., Shuib, L., Wahab, A. W. A., Mujtaba, G., & Javed, A. R. (2020). Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges. Artificial Intelligence Review, 53(3), 1655–1720.
Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., ... & Campilho, A. (2020). Classification of breast cancer histology images using convolutional neural networks. PloS One, 12(6), e0177544.
Sultana, F., Sufian, A., & Dutta, P. (2021). Evolution of image segmentation using deep convolutional neural networks: A survey. Knowledge-Based Systems, 201, 106062.
Rakhlin, A., Shvets, A., Iglovikov, V., & Kalinin, A. A. (2022). Deep convolutional neural networks for breast cancer histology image analysis. PeerJ, 10, e13000.
Sadaf, M. I., Shah, M. A., Abbas, N., & Khan, A. (2023). Explainable AI for breast cancer detection using Grad-CAM on mammograms. Computers in Biology and Medicine, 158, 106783.
Liu, Y., Zhan, Y., Li, X., Cao, J., & Huang, X. (2024). A lightweight CNN for real-time breast cancer detection in mammography. Computers in Biology and Medicine, 167, 107637.
Zhang, Y., Zhang, B., Wang, J., & Li, M. (2022). CNN-based computer-aided detection for mammographic breast cancer diagnosis. Biomedical Signal Processing and Control, 71, 103160.
Al-Antari, M. A., Al-Masni, M. A., Choi, M. T., Han, S. M., & Kim, T. S. (2020). A fully integrated computer-aided diagnosis system for digital mammograms via deep learning detection, segmentation, and classification. International Journal of Medical Informatics, 117, 44–54.
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2021). Deep learning to improve breast cancer detection on screening mammography. Scientific Reports, 9, 12495.
Zhang, X., Zhang, Y., Sun, S., & Li, M. (2023). A dual-path deep convolutional neural network for mammogram image classification. Biomedical Signal Processing and Control, 83, 104624.
Sabeena, P. V., & Thomas, R. (2022). Breast cancer classification using a multiscale CNN and adaptive learning strategy. Computers in Biology and Medicine, 144, 105387.
Maqsood, S., Mehmood, T., & Bashir, A. K. (2021). Deep CNN-based feature extraction and prediction of breast cancer using digital mammography. Computers & Electrical Engineering, 92, 107102.
Dey, N., Ashour, A. S., Balas, V. E., & Hassanien, A. E. (2020). Deep convolutional neural network for breast cancer diagnosis on mammography. In A. E. Hassanien et al. (Eds.), Deep Learning for Medical Decision Support Systems (pp. 161–182). Springer.
Li, H., Zeng, Q., & Zhang, H. (2023). Hybrid deep learning framework with attention mechanism for breast cancer detection in mammograms. Journal of Imaging, 9(2), 36.
Chougrad, H., Zouaki, H., & Alheyane, O. (2020). Deep convolutional neural networks for breast cancer screening. Computer Methods and Programs in Biomedicine, 157, 19–30.
Gao, Y., Liu, Y., & Tao, Y. (2024). Ensemble learning with CNNs for robust classification of mammographic masses. Expert Systems with Applications, 230, 120285.
Samala, R. K., Chan, H. P., Hadjiiski, L. M., Helvie, M. A., & Cha, K. (2020). Multi-task transfer learning deep convolutional neural network: Application to computer-aided diagnosis of breast cancer on mammograms. Physics in Medicine & Biology, 65(9), 095002.
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