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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2018

Autores

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

Ata de Conferência Internacional

Título: ICIAR

Páginas: 763-770

Indexação

Scopus - 0 Citações

INSPEC

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 8