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Published on: December 15, 2014
The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of
Érika G de Assis1, Zenilton K G do Patrocínio1, Cristiane Neri Nobre1
1Pontifical Catholic University of Minas Gerais, Institute of Exact Sciences and Informatics, Graduate Program in Informatics, Belo Horizonte, MG, Brazil.
Abstract:
This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.
