Abstract
Breast cancer is one of the leading causes of female death worldwide. The histological analysis of breast tissue allows for the differentiation of the tissue suspected to be abnormal into four classes: normal tissue, benign tumor, in situ carcinoma and invasive carcinoma. Automatic diagnostic systems can help in that task. In this sense, this work propose a deep neural network approach using transfer learning to classify breast cancer histology images. First, the added top layers are trained and a second fine-tunning is done on some feature extraction layers that are frozen previously. The used network is an Inception Resnet V2. In order to overcome the lack of data, data augmentation is performed too. This work is a suggested solution for the ICIAR 2018 BACH-Challenge and the accuracy is 0.76 in the blind test set.
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Acknowledgments
This work is financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and the European Regional Development Fund (ERDF), within the project “NanoSTIMA: Macro-to-Nano Human Sensing: Towards Integrated Multimodal Health Monitoring and Analytics/NORTE-01-0145-FEDER-000016”.
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A Appendix
A Appendix
Each class has images numbered from 1 to 100. In each class, the following images were used to construct the datasets: Test set - 2, 3, 4, 15, 20, 27, 38, 39, 42, 44, 47, 54, 60, 61, 67, 69, 75, 80, 93 and 96; Validation set - 11, 18, 21, 23, 45, 49, 55, 87, 89 and 99; Training set - remaining images.
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Ferreira, C.A. et al. (2018). Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_86
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DOI: https://doi.org/10.1007/978-3-319-93000-8_86
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