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Updated: Feb 9, 2026
07:45
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
Published on: May 18, 2020
7.1K
Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network
Payam Zarbakhsh1, Abdoljalil Addeh2
1Department of Electrical and Electronic Engineering, Eastern Mediterranean University, KKTC, Via Mersin-10, Gazimağusa, Turkey.
Journal of Cancer Research and Therapeutics
|June 13, 2018
Summary
This study introduces an intelligent system for breast cancer tumor recognition. The system achieved 99.59% accuracy, improving early diagnosis for better survival rates.
Area of Science:
- Medical Informatics
- Artificial Intelligence
- Biomedical Engineering
Background:
- Breast cancer poses a significant mortality risk, particularly in developing nations.
- Early diagnosis is critical for enhancing patient survival rates in breast cancer cases.
Purpose of the Study:
- To propose an intelligent system for accurate breast cancer tumor type recognition.
Main Methods:
- A novel graph-based feature selection approach was employed.
- A radial basis function neural network (RBFNN) was utilized as the classifier.
- The artificial bee colony (ABC) algorithm optimized RBFNN parameters.
Main Results:
- The system achieved a high accuracy of 99.59% in classifying breast tumors.
- Performance was validated using the Wisconsin Breast Cancer Database (WBCD).
Conclusions:
- The developed intelligent system demonstrates high recognition accuracy for breast cancer tumor types.
- The system is recommended for practical application in breast cancer diagnosis.
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