Title:Improving Breast Cancer Detection with Convolutional Neural Networks and
Modified ResNet Architecture
Volume: 20
Author(s): Javad Nouri Pour, Mohammad Ali Pourmina*Mohammad Naser Moghaddasi
Affiliation:
- Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
Keywords:
Breast cancer, Convolutional neural network, ResNet, ResBlock, Clinical applications, WSIs tissue.
Abstract:
Background:
The pathogenesis of breast cancer is characterized by dysregulated cell proliferation, leading to the formation of a neoplastic mass. Conventional
methodologies for analyzing carcinomatous distal areas within whole-slide images (WSIs) tissue regions may lack comprehensive insights.
Purpose:
This study aims to introduce an innovative methodology based on convolutional neural networks (CNN), specifically employing a CNN Modified
ResNet architecture for breast cancer detection. The research seeks to address the limitations of existing approaches and provide a robust solution
for the comprehensive analysis of tissue regions.
Methods:
The dataset utilized in this study comprises approximately 275,000 RGB image patches, each standardized at 50x50 pixels. The CNN Modified
ResNet architecture is implemented, and a comparative evaluation against diverse architectures is conducted. Rigorous validation tests employing
established performance metrics are carried out to assess the proposed methodology.
Results:
The proposed architecture achieves a notable 89% accuracy in breast cancer detection, surpassing alternative methods by 2%. The results signify
the efficacy and superiority of the CNN Modified ResNet model in analyzing carcinomatous distal areas within WSIs tissue regions.
Conclusion:
In conclusion, this study demonstrates the potential of the CNN Modified ResNet architecture as an effective tool for breast cancer detection. The
enhanced accuracy and comprehensive analysis capabilities make it a promising approach for advancing the understanding of neoplastic masses in
WSIs tissue regions. Further research and validation could solidify its role in clinical applications and diagnostic procedures.
