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Boosting Breast Cancer Detection Using Convolutional Neural Network
Saad Awadh Alanazi1, M M Kamruzzaman1, Md Nazirul Islam Sarker2
1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia.
Journal of Healthcare Engineering
|April 22, 2021
Summary
This study introduces a convolutional neural network (CNN) for breast cancer detection in whole-slide images. The CNN method achieved 87% accuracy, outperforming traditional machine learning algorithms by 9% for improved diagnosis.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Breast cancer is a prevalent and life-threatening disease affecting women globally.
- Accurate and early detection is crucial for effective treatment and improved patient outcomes.
- Current diagnostic methods can be subject to human error and variability.
Purpose of the Study:
- To develop and evaluate a convolutional neural network (CNN) system for automated breast cancer identification.
- To analyze hostile ductal carcinoma tissue zones within whole-slide images (WSIs).
- To compare the performance of CNN architectures against traditional machine learning (ML) algorithms.
Main Methods:
- Utilized various convolutional neural network (CNN) architectures for image analysis.
- Trained and validated the models on a large dataset of approximately 275,000 RGB image patches (50x50 pixels).
- Compared CNN performance against established machine learning (ML) algorithms using quantitative performance measures.
Main Results:
- The proposed CNN system achieved an accuracy of 87% in breast cancer identification.
- The CNN system demonstrated a 9% improvement in accuracy compared to ML algorithms (87% vs. 78%).
- The system successfully identified cancerous tissue zones in whole-slide images.
Conclusions:
- The developed CNN system significantly enhances the accuracy of automated breast cancer detection.
- This AI-driven approach has the potential to reduce diagnostic errors and improve patient care.
- The findings support the integration of advanced AI techniques in histopathological analysis.
