An Optimized and Attention-Enhanced Convolutional Neural Network for Accurate Breast Cancer Diagnosis

Year 2025, Volume: 12 Issue: 4, 1121 - 1148, 31.12.2025

Ragheed Mokhtar , Mahir Kaya

https://doi.org/10.54287/gujsa.1804559

Abstract

Breast cancer continues to be one of the most prevalent as well as life-threatening malignancies because it affects women globally. Ultrasound images get diagnostic interpretation that is customarily variable, with limitations in sensitivity and specificity. For breast ultrasound image classification, we propose a novel fully optimized Convolutional Neural Network (CNN) architecture. This study's classification includes three clinically important categories: normal, malignant, and benign. We used the Breast Ultrasound Images (BUSI) dataset, and we tackled intrinsic issues like small dataset size, class imbalance, and inter-class similarity via a diverse augmentation pipeline. The proposed CNN architecture integrates state-of-the-art deep learning techniques that include hierarchical feature extraction, attention mechanisms, batch normalization, dropout regularization, along with adaptive learning rates. The best-performing model reached a test accuracy of 99.33%, demonstrating the effectiveness of the proposed approach. This performance gain was obtained through systematic hyperparameter optimization involving the number of convolutional layers, learning rates, batch sizes, and input image resolutions. Although trained without pre-existing knowledge, the model showed similar performance when evaluated against popular transfer learning models like MobileNetV2, VGG16, VGG19, InceptionV3, ResNet50, EfficientNetB0, DenseNet121, and Xception. The proposed model, interpretable through Grad-CAM, effectively focuses on disease-related regions and enables specialists to make fast and reliable decisions.

Keywords

Breast Cancer , Breast Ultrasound Imaging , Convolutional Neural Networks , Hyperparameters tuning

References

• Alotaibi, M., Aljouie, A., Alluhaidan, N., Qureshi, W., Almatar, H., Alduhayan, R., Alsomaie, B., & Almazroa, A. (2023). Breast cancer classification based on convolutional neural network and image fusion approaches using ultrasound images. Heliyon, 9(11), e22406. https://doi.org/10.1016/j.heliyon.2023.e22406
• Alrubaie, H., Aljobouri, H. K., AL-Jobawi, Z. J., & Çankaya, I. (2023). Convolutional neural network deep learning model for improved ultrasound breast tumor classification. Al-Nahrain Journal for Engineering Sciences, 26(2), 57–62. https://doi.org/10.29194/njes.26020057
• Arooj, S., Zubair, M., Khan, M. F., Alissa, K., Khan, M. A., & Mosavi, A. (2022). Breast cancer detection and classification empowered with transfer learning. Frontiers in Public Health, 10, 924432. https://doi.org/10.3389/fpubh.2022.924432
• Boulenger, A., Luo, Y., Zhang, C., Zhao, C., Gao, Y., Xiao, M., Zhu, Q., & Tang, J. (2023). Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images. Medical and Biological Engineering and Computing, 61(2), 567–578. https://doi.org/10.1007/s11517-022-02728-4
• Cheyi, J., & Çetin-Kaya, Y. (2024). Advanced CNN-based classification and segmentation for enhanced breast cancer ultrasound imaging. Gazi University Journal of Science Part A: Engineering and Innovation, 11(4), 647–667. https://doi.org/10.54287/gujsa.1529857
• Ciobotaru, A., Bota, M. A., Goța, D. I., & Miclea, L. C. (2023). Multi-Instance Classification of Breast Tumor Ultrasound Images Using Convolutional Neural Networks and Transfer Learning. Bioengineering, 10(12). https://doi.org/10.3390/bioengineering10121419
• Çetin-Kaya, Y. (2024). Equilibrium optimization-based ensemble CNN framework for breast cancer multiclass classification using histopathological image. Diagnostics, 14(19), 2253. https://doi.org/10.3390/diagnostics14192253
• Daoud, M. I., Abdel-Rahman, S., & Alazrai, R. (2019, November 26-29). Breast ultrasound image classification using a pre-trained convolutional neural network. In: Proceedings of the 2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) (pp. 167–171), Sorrento, Italy. https://doi.org/10.1109/SITIS.2019.00037
• Daoud, M. I., Abdel-Rahman, S., Bdair, T. M., Al-Najar, M. S., Al-Hawari, F. H., & Alazrai, R. (2020). Breast tumor classification in ultrasound images using combined deep and handcrafted features. Sensors, 20(23), 6838. https://doi.org/10.3390/s20236838
• Eroğlu, Y., Yildirim, M., & Çinar, A. (2021). Convolutional neural networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Computers in Biology and Medicine, 133, 104407. https://doi.org/10.1016/j.compbiomed.2021.104407
• Fujioka, T., Katsuta, L., Kubota, K., Mori, M., Kikuchi, Y., Kato, A., Oda, G., Nakagawa, T., & Tateishi, U (2020). Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks. Ultrasonic Imaging, 42(4–5), 213–220. https://doi.org/10.1177/0161734620932609
• Güldoğan, E., Ucuzal, H., Küçükakçalı, Z., & Çolak, C. (2021). Transfer learning-based classification of breast cancer using ultrasound images. Middle Black Sea Journal of Health Science, 7(1), 74–80. https://doi.org/10.19127/mbsjohs.876667
• Hijab, M. A., Rushdi, M., Gomaa, M. M., & Eldeib, A. (2019, October 17-19). Breast cancer classification in ultrasound images using transfer learning. In: Proceedings of the 2019 Fifth International Conference on Advances in Biomedical Engineering (ICABME), Tripoli, Lebanon https://doi.org/10.1109/ICABME47164.2019.8940291
• Karthik, R., Menaka, R., Kathiresan, G. S., Anirudh, M., & Nagharjun, M. (2022). Gaussian dropout based stacked ensemble CNN for classification of breast tumor in ultrasound images. IRBM, 43(6), 715–733. https://doi.org/10.1016/j.irbm.2021.10.002
• Kaya, M., & Çetin-Kaya, Y. (2024). A novel ensemble learning framework based on a genetic algorithm for the classification of pneumonia. Engineering Applications of Artificial Intelligence, 133, 108494. https://doi.org/10.1016/j.engappai.2024.108494
• Kaya, M. (2025). Efficient diagnosis of retinal diseases using convolutional neural networks. Gazi University Journal of Science Part A: Engineering and Innovation, 12(1), 15–35. https://doi.org/10.54287/gujsa.1592915
• Latif, G., Butt, M. O., Al Anezi, F. Y., & Alghazo, J. (2020, October 14-15). Ultrasound image despeckling and detection of breast cancer using deep CNN. In: Proceedings of the 2020 RIVF International Conference on Computing and Communication Technologies (RIVF), Ho Chi Minh City, Vietnam. https://doi.org/10.1109/RIVF48685.2020.9140767
• Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
• Liu, H., Cui, G., Luo, Y., Guo, Y., Zhao, L., Wang, Y., Subaşı, A., Doğan, S., & Tuncer, T. (2022). Artificial intelligence-based breast cancer diagnosis using ultrasound images and grid-based deep feature generator. International Journal of General Medicine, 15, 2271–2282. https://doi.org/10.2147/IJGM.S347491
• Nikmah, T. L., Syafei, R. M., & Anisa, D. N. (2024). Inception ResNet v2 for early detection of breast cancer in ultrasound images. Journal of Information System Exploration and Research, 2(2). https://doi.org/10.52465/joiser.v2i2.439
• Özatılgan, A., & Kaya, M. (2024). A lightweight convolutional neural network for classification of brain tumors using magnetic resonance imaging. Sakarya University Journal of Computer and Information Sciences, 7(3), 482–493. https://doi.org/10.35377/saucis.1518139
• Pathan, R. K., Alam, F. I., Yasmin, S., Hamd, Z. Y., Aljuaid, H., Khandaker, M. U., & Lau, S. L. (2022). Breast cancer classification by using multi-headed convolutional neural network modeling. Healthcare, 10(12), 2367. https://doi.org/10.3390/healthcare10122367
• Saba, T., Abunadi, I., Sadad, T., Khan, A. R., & Bahaj, S. A. (2022). Optimizing the transfer-learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images. Microscopy Research and Technique, 85, 1444–1453. https://doi.org/10.1002/jemt.24008
• Sahu, P., Das, K., & Meher, S. (2024). An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images. Biomedical Signal Processing and Control, 87, 105377. https://doi.org/10.1016/j.bspc.2023.105377
• Sivanandan, R., & Jayakumari, J. (2021). A new CNN architecture for efficient classification of ultrasound breast tumor images with activation map clustering based prediction validation. Medical & Biological Engineering & Computing, 59(4), 957–968. https://doi.org/10.1007/s11517-021-02357-3
• Uysal, F., & Köse, M. M. (2022). Classification of breast cancer ultrasound images with deep learning-based models. Engineering Proceedings, 31(1). https://doi.org/10.3390/ASEC2022-13791
• Yap, M. H., Pons, G., Marti, J., Ganau, S., Sentis, M., Zwiggelaar, R., Davision, A. K., & Marti, R. (2018). Automated breast ultrasound lesions detection using convolutional neural networks. IEEE Journal of Biomedical and Health Informatics, 22(4), 1218–1226. https://doi.org/10.1109/JBHI.2017.2731873