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

• Type: Article in International Conference Proceedings Book
• Date: 2018

Authors

Carlos A. Ferreira (Author) Other; Tânia Melo (Author) Other; Patrick Sousa (Author) Other; Maria Inês Meyer (Author) Other; Elham Shakibapour (Author) Other; Pedro Costa (Author) Other; Aurélio Campilho (Author) FEUP

Conference proceedings International

Title: ICIAR

Pages: 763-770

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Scopus - 0 Citations

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Other information

Authenticus ID: P-00N-ZM5

DOI: 10.1007/978-3-319-93000-8_86

Abstract (EN): 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. © 2018, Springer International Publishing AG, part of Springer Nature.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8