Application of VGG16 Transfer Learning for Breast Cancer Detection

被引：0
作者：
Fatima, Tanjim [1 ]
Soliman, Hamdy [1 ]
机构：
[1] Department of Computer Science, New Mexico Tech, Socorro, 87801, NM
来源：
Information (Switzerland) | 2025年 / 16卷 / 03期
关键词：
breast cancer detection; deep learning; histopathology; image classification; transfer learning; VGG16;
D O I：
10.3390/info16030227
中图分类号：
学科分类号：
摘要：
Breast cancer is among the primary causes of cancer-related deaths globally, highlighting the critical need for effective and early diagnostic methods. Traditional diagnostic approaches, while valuable, often face limitations in accuracy and accessibility. Recent advancements in deep learning, particularly transfer learning, provide promising solutions for enhancing diagnostic precision in breast cancer detection. Due to the limited capability of the BreakHis dataset, transfer learning was utilized to advance the training of our new model with the VGG16 neural network model, well trained on the rich ImageNet dataset. Moreover, the VGG16 architecture was carefully modified, including the fine-tuning of its layers, yielding our new model: M-VGG16. The new M-VGG16 model is designed to carry out the binary cancer/benign classification of breast samples effectively. The experimental results of our M-VGG16 model showed it achieved high validation accuracy (93.68%), precision (93.22%), recall (97.91%), and a high AUC (0.9838), outperforming other peer models in the same field. This study validates the VGG16 model’s suitability for breast cancer detection via transfer learning, providing an efficient, adaptable framework for improving diagnostic accuracy and potentially enhancing breast cancer detection. Key breast cancer detection challenges and potential M-VGG16 model refinements are also discussed. © 2025 by the authors.
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  • [31] Krogh A., Hertz J.A., A simple weight decay can improve generalization, Adv. Neural Inf. Process. Syst, 4, pp. 950-957, (1992)
  • [32] Spanhol F.A., Oliveira L.S., Petitjean C., Heutte L., A dataset for breast cancer histopathological image classification, IEEE Trans. Biomed. Eng, 63, pp. 1455-1462, (2015)
  • [33] Bayramoglu N., Kannala J., Heikkila J., Deep Learning for Magnification Independent Breast Cancer Histopathology Image Classification, Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), pp. 2440-2445, (2016)
  • [34] Cui Y., Jia M., Lin T.Y., Song Y., Belongie S., Class-balanced loss based on effective number of samples, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9268-9277
  • [35] King G., Zeng L., Logistic regression in rare events data, Political Anal, 9, pp. 137-163, (2001)
  • [36] Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V., Et al., Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res, 12, pp. 2825-2830, (2011)
  • [37] Liu S., Deng W., Very deep convolutional neural network based image classification using small training sample size, Proceedings of the 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 730-734, (2015)
  • [38] Lin M., Chen Q., Yan S., Network in network, arXiv, (2013)
  • [39] Nair V., Hinton G.E., Rectified linear units improve restricted Boltzmann machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp. 807-814
  • [40] Goodfellow I., Bengio Y., Courville A., Deep Learning, (2016)
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