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Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2

  • Carlos A. Ferreira
  • Tânia Melo
  • Patrick Sousa
  • Maria Inês Meyer
  • Elham Shakibapour
  • Pedro Costa
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10882)

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.

Keywords

Breast cancer diagnosis Breast histology images classification Convolutional neural network Inception resnet v2 Transfer learning Data augmentation Fine-tuning 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.INESC-TEC - Institute for Systems and Computer Engineering, Technology and SciencePortoPortugal
  2. 2.Faculty of EngineeringUniversity of PortoPortoPortugal

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