Breast Cancer Classification Using Densenet121 And K-Means Segmentation With Augmented Data
Abstract
Breast cancer remains a significant global health challenge, necessitating improved diagnostic approaches for early detection and treatment. This study presents an optimized deep learning framework that integrates DenseNet121 with K-Means clustering for enhanced segmentation and feature extraction in breast cancer histopathology images. The BreakHis dataset, comprising 7,909 images at varying magnifications (40×, 100×, 200×, and 400×), was employed for model training and evaluation. Image preprocessing involved histogram equalization and augmentation techniques, including rotation and contrast adjustment, to enhance model robustness. The DenseNet121 model was fine-tuned using transfer learning with pre-trained ImageNet weights, and hyperparameters were optimized to improve classification performance. The proposed model achieved an accuracy of 95.21%, surpassing conventional architectures such as ResNet50 (92.4%) and Xception (88.08%). Additionally, an external validation on the BACH dataset demonstrated an accuracy of 92.10%, reinforcing the model's generalizability. Comparative analysis and ablation studies confirmed the significance of K-Means clustering in improving classification outcomes. Future research will focus on multimodal imaging techniques and Explainable AI (XAI) to enhance interpretability and clinical applicability.References
GLOBOCAN. (2020). Global cancer statistics 2020. International Agency for Research on Cancer. https://www.iarc.fr
World Health Organization (WHO). (2020). Breast cancer fact sheet. World Health Organization. https://www.who.int/news-room/fact-sheets/detail/breast-cancer
American Cancer Society. (n.d.). Statistics of Breast Cancer with Implication. American Cancer Society. https://www.cancer.org.
Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A., Ciompi, F., Ghafoorian, M., ... & van Ginneken, B. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of Cancer: The Next Generation. Cell, 144(5), 646-674.
Zhang, Y., Liu, S., Zhang, Q., & Song, J. (2020). A deep convolutional neural network for breast cancer detection using mammograms. Journal of Medical Imaging, 7(1), 015001.
Li, X., Hu, Z., Chen, Y., & Zhang, J. (2018). Multi-view CAD system combining mammography and ultrasound images for breast cancer detection. Computer Methods and Programs in Biomedicine, 162, 15-23.
Saha, M., Bhattacharya, T., Choudhury, A., & Chakraborty, C. (2016). Computer-aided diagnosis of breast cancer using MRI images and classification using deep learning. International Journal of Image and Data Fusion, 7(1), 60-70.
Cheng, J., Ni, D., Chou, Y., Qin, J., & Heng, P. A. (2017). Computer-aided detection system for early-stage breast cancer using digital breast tomosynthesis. Journal of Digital Imaging, 30(5), 662-669.
Kim, H. E., Kim, H. H., Han, B. K., & Kim, K. H. (2019). Automated breast cancer detection in digital mammograms using deep learning: Clinical performance in a large clinical dataset. Radiology, 290(1), 81-89.
Yousefi, S., Nosrati, M. S., & Hamarneh, G. (2017). Integrating mammography and thermography for breast cancer detection using deep learning. IEEE Transactions on Medical Imaging, 36(5), 1148-1157.
Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018). Detecting and classifying lesions in mammograms with Deep Learning. Scientific Reports, 8(1), 4165.
Suzuki, S., Abe, H., MacMahon, H., & Doi, K. (2017). Image processing and analysis: Applications in mammographic breast cancer detection. Academic Radiology, 14(8), 1057-1071.
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019). Deep learning to improve breast cancer detection on screening mammography. Scientific Reports, 9(1), 12495.
Wang, J., Yang, X., Cai, H., Tan, W., & Jin, C. (2020). Discrimination of breast cancer with ensemble learning using multi-level features of digital mammograms. Computers in Biology and Medicine, 121, 103760.
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4700–4708). IEEE.
Guan, X., & Loew, M. (2017). Breast Cancer Detection Using Transfer Learning in Convolutional Neural Networks. IEEE Transactions on Biomedical Engineering, 64(9), 2220-2229
Joshi, A., Kumar, R., & Patel, M. (2023). Breast cancer classification using deep learning models: A comparative study. Journal of Medical Imaging and Health Informatics, 13(4), 123-135
Masud, M., Eldin Rashed, M., & Hossain, M. S. (2022). Breast cancer detection using deep learning techniques: A comparative analysis. Journal of Artificial Intelligence in Medicine, 18(2), 45-58
Ajagbe, S. A., & Adigun, M. O. (2023). Deep learning techniques for detection and prediction of pandemic diseases: a systematic literature review. Multimedia Tools Application, 2023, 1-35. doi:https://doi.org/10.1007/s11042-023-15805-z
DOI:
https://doi.org/10.31449/inf.v49i27.8332Downloads
Published
How to Cite
Issue
Section
License
I assign to Informatica, An International Journal of Computing and Informatics ("Journal") the copyright in the manuscript identified above and any additional material (figures, tables, illustrations, software or other information intended for publication) submitted as part of or as a supplement to the manuscript ("Paper") in all forms and media throughout the world, in all languages, for the full term of copyright, effective when and if the article is accepted for publication. This transfer includes the right to reproduce and/or to distribute the Paper to other journals or digital libraries in electronic and online forms and systems.
I understand that I retain the rights to use the pre-prints, off-prints, accepted manuscript and published journal Paper for personal use, scholarly purposes and internal institutional use.
In certain cases, I can ask for retaining the publishing rights of the Paper. The Journal can permit or deny the request for publishing rights, to which I fully agree.
I declare that the submitted Paper is original, has been written by the stated authors and has not been published elsewhere nor is currently being considered for publication by any other journal and will not be submitted for such review while under review by this Journal. The Paper contains no material that violates proprietary rights of any other person or entity. I have obtained written permission from copyright owners for any excerpts from copyrighted works that are included and have credited the sources in my article. I have informed the co-author(s) of the terms of this publishing agreement.
Copyright © Slovenian Society Informatika
