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Parallel Structure Deep Neural Network Using CNN and RNN with an Attention Mechanism for Breast Cancer Histology

Hongdou Yao¹, Xuejie Zhang¹, Xiaobing Zhou¹

¹School of Information Science and Engineering, Yunnan University, Kunming 650091, China.

|December 5, 2019

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This study introduces a novel deep learning model for classifying breast biopsy images. The advanced model significantly improves the accuracy of identifying normal tissues, benign lesions, and various carcinoma types.

Area of Science:

Medical Imaging
Computational Pathology
Artificial Intelligence in Medicine

Background:

Accurate classification of hematoxylin-eosin-stained breast biopsy images is crucial for effective cancer diagnosis and treatment.
Existing deep learning models often employ serial architectures for feature extraction, which may not capture complex image patterns optimally.

Purpose of the Study:

To develop and evaluate a novel deep learning model for classifying breast biopsy images into four distinct categories: normal tissues, benign lesions, in situ carcinomas, and invasive carcinomas.
To enhance image feature extraction and classification accuracy using a parallel CNN-RNN structure and advanced deep learning techniques.

Main Methods:

A parallel deep learning architecture combining a convolutional neural network (CNN) and a recurrent neural network (RNN) for simultaneous image feature extraction.

Keywords:

DenseNet LSTM attention biopsy image breast cancer switchable normalization targeted dropout test time augmentation

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Integration of a perceptron attention mechanism, adapted from natural language processing, to unify features from CNN and RNN components.

Implementation of switchable normalization in convolution layers and targeted dropout in fully connected layers for improved model performance and regularization.

Utilized model fusion and test-time augmentation on three distinct breast biopsy image datasets during the testing phase.

Main Results:

The proposed deep learning model demonstrated superior performance in classifying breast biopsy images compared to existing state-of-the-art methods.
The parallel CNN-RNN structure, coupled with the attention mechanism, effectively captured complex image features for accurate classification.
Advanced techniques like switchable normalization, targeted dropout, model fusion, and test-time augmentation contributed to the model's enhanced accuracy.

Conclusions:

The novel deep learning model offers a significant advancement in the automated classification of breast biopsy images.
The parallel architecture and specialized mechanisms provide a robust framework for improving diagnostic accuracy in computational pathology.
This approach holds potential for enhancing the efficiency and reliability of breast cancer diagnosis.