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Related Concept Videos

Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
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Convolution Properties I01:20

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Convolution computations can be simplified by utilizing their inherent properties.
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Related Experiment Video

Updated: Jan 29, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
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Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural

Md Zahangir Alom1, Chris Yakopcic2, Mst Shamima Nasrin2

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

Journal of Digital Imaging
|February 14, 2019
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Summary

A novel Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model enhances breast cancer classification. This deep learning approach achieves superior performance on public datasets compared to existing methods.

Keywords:
Breast cancer recognitionComputational pathologyDCNNDeep learningIRRCNNMedical imaging

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Deep Convolutional Neural Networks (DCNNs) excel in medical image analysis.
  • Breast cancer remains a significant global health concern for women.
  • Accurate classification is crucial for effective breast cancer treatment.

Purpose of the Study:

  • To propose and evaluate a new deep learning model, the Inception Recurrent Residual Convolutional Neural Network (IRRCNN), for breast cancer classification.
  • To assess the IRRCNN model's performance against established methods on public breast cancer datasets.

Main Methods:

  • Developed an IRRCNN model integrating Inception-v4, ResNet, and RCNN architectures.
  • Applied the IRRCNN model to two public datasets: BreakHis and the Breast Cancer classification challenge 2015.
  • Compared IRRCNN performance against existing machine learning and deep learning approaches at various classification levels (image-based, patch-based, image-level, patient-level).

Main Results:

  • The IRRCNN model demonstrated superior classification performance.
  • Achieved higher sensitivity, Area Under the Curve (AUC), ROC curve metrics, and global accuracy compared to existing methods.
  • Consistent superior performance was observed across both evaluated datasets.

Conclusions:

  • The proposed IRRCNN model offers a powerful and effective deep learning solution for breast cancer classification.
  • IRRCNN shows significant potential to improve diagnostic accuracy in breast cancer detection.
  • This advanced DCNN approach represents a promising advancement in the field of medical image analysis for oncology.