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

By Md. Zahangir Alom, C. Yakopcic, Tarek M. Taha, Vijayan K. Asari. Journal of digital imaging, 2018

295 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/644482c6c6ca800ccc4ef07505e34dbde8cefcb4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Usha R. Acharya
2. Filippo Molinari
3. Kristen M. Meiburger
4. Massimo Salvi

In Computer Vision and Pattern Recognition

1. Usha R. Acharya
2. Filippo Molinari
3. Kristen M. Meiburger
4. Massimo Salvi

Citations in the corpus

• The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, background): “An exponentially growing field in computerized approaches are methods based on deep learning, with studies showing how deep neural networks have reached the performance of state-of-the-art methods in almost all medical imaging fields [1,2].”
• Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “This is an advanced level of DL used in automating the responsibility involved in image processing (Alom, Yakopcic, Nasrin, Taha, & Asari, 2019).”