Abstract
In this study, we focus on the problem of computer-aided diagnosis of breast cancer using cytological images of fine needle biopsies. We explore the potential of modern deep neural network architectures by comparing five different convolutional neural networks trained to classify the specimen as either benign or malignant. For experimentation, we use 550 cytological images of fine needle biopsies from 50 patients, balanced between benign and malignant cases, acquired at the University Hospital in Zielona Góra, Poland. We found that the convolutional neural network Inception-v3 is the best model, reaching 91.86% accuracy and 0.97 value for area under the curve (AUC).
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- 1.
There is a discussion whether palpation should or should not be recommended.
- 2.
Although FNB is a type of biopsy, it is also classified as a cytology examination.
- 3.
All of the models have been trained on a workstation with Intel i7-8700K CPU, 64 GB of RAM and 2 NVIDIA GeForce GTX 1080 Ti GPUs, each with 11 GB of memory.
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Acknowledgments
T. Fevens, A. Krzyzak and B. Miselis were supported by the Natural Sciences and Engineering Research Council of Canada under Grants RGPIN-04929-2014 and RGPIN-2015-06412. M. Kowal and R. Monczak were supported by the National Science Centre, Poland (grant no. 2015/17/B/ST7/03704).
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Miselis, B., Fevens, T., Krzyżak, A., Kowal, M., Monczak, R. (2020). Deep Neural Networks for Breast Cancer Diagnosis: Fine Needle Biopsy Scenario. In: Korbicz, J., Maniewski, R., Patan, K., Kowal, M. (eds) Current Trends in Biomedical Engineering and Bioimages Analysis. PCBEE 2019. Advances in Intelligent Systems and Computing, vol 1033. Springer, Cham. https://doi.org/10.1007/978-3-030-29885-2_12
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