Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/8291
Title: Classification of Breast Cancer Histology Images Through Transfer Learning Using a Pre-trained Inception Resnet V2
Authors: Carlos Alexandre Ferreira
Tânia Fernandes Melo
Sousa,P
Maria Inês Meyer
Elham Shakibapour
Costa,P
Aurélio Campilho
Issue Date: 2018
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. © 2018, Springer International Publishing AG, part of Springer Nature.
URI: http://repositorio.inesctec.pt/handle/123456789/8291
http://dx.doi.org/10.1007/978-3-319-93000-8_86
metadata.dc.type: Publication
conferenceObject
Appears in Collections:C-BER - Articles in International Conferences

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