Breast Cancer Diagnosis with Transfer Learning and Global Pooling

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Authors Ralph Deters, Michal J. Wesolowski, Kevin A. Schneider, Peyman Hosseinzadeh Kassani, Sara Hosseinzadeh Kassani
Journal/Conference Name ICTC 2019 - 10th International Conference on ICT Convergence: ICT Convergence Leading the Autonomous Future
Paper Category
Paper Abstract Breast cancer is one of the most common causes of cancer-related death in women worldwide. Early and accurate diagnosis of breast cancer may significantly increase the survival rate of patients. In this study, we aim to develop a fully automatic, deep learning-based, method using descriptor features extracted by Deep Convolutional Neural Network (DCNN) models and pooling operation for the classification of hematoxylin and eosin stain (H&E) histological breast cancer images provided as a part of the International Conference on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer Histology (BACH) Images. Different data augmentation methods are applied to optimize the DCNN performance. We also investigated the efficacy of different stain normalization methods as a pre-processing step. The proposed network architecture using a pre-trained Xception model yields 92.50% average classification accuracy.
Date of publication 2019
Code Programming Language Jupyter Notebook

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