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J Digit Imaging. 2019 Aug;32(4):605-617. doi: 10.1007/s10278-019-00182-7.

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network.

Author information

1
Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA. alomm1@udayton.edu.
2
Department of Electrical and Computer Engineering, University of Dayton, Dayton, OH, USA.

Abstract

The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning-based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.

KEYWORDS:

Breast cancer recognition; Computational pathology; DCNN; Deep learning; IRRCNN; Medical imaging

PMID:
30756265
PMCID:
PMC6646497
[Available on 2020-08-01]
DOI:
10.1007/s10278-019-00182-7

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