Classification of Breast Cancer Masses using Non-Linear Quadratic Support Vector Machine and Comparison with Self-Organizing Neural Network
Subject Areas : Artificial Intelligent
Soodeh Bakhshandeh
1
,
Seyede Monire Atyabi
2
,
Sahar Saberi
3
1 - Department of Computer Engineering, East Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering. South Tehran Branch Islamic Azad University. Tehran. Iran
3 - Department of Computer Engineering, East Tehran Branch, Islamic Azad University, Tehran
Keywords: Breast Cancer, Wisconsin Data, Support Vector Machine, Quadratic Kernel.,
Abstract :
Breast cancer is the second most common cancer after lung cancer and the fifth leading cause of death in women. In less developed countries, breast cancer is the most important cause of death. In this disease, the cells of the breast tissue change and divide into multiple cells and cause a lump. If breast cancer is in the early stages, treatment is possible. There are many treatment methods such as surgery to remove the defective area, drug therapy, radiation therapy, chemotherapy, hormone therapy, and immunotherapy. These treatments have the potential to save lives when administered in the early stages. From the above explanations, it can be seen that early detection of breast cancer is very important and in this research, an attempt has been made to identify suspected cancer data with the quadratic support vector machine method and based on the features extracted from valid and numerous MRI images. Let's classify so that the process of diagnosing the disease in the early stages is easier and faster. The results showed that 356 out of 357 malignant data and 202 out of 211 benign data were correctly classified. The classification accuracy of malignant data was 99.7% and the classification accuracy of benign data was 97.5%, and finally the overall classification accuracy was 98.2%, which indicates the optimal performance of this method in breast cancer data classification.
Developing a method to diagnose breast cancer with the aim of diagnosing the disease in the early stages
Applying support vector machine with quadratic kernel with the aim of reducing classification time
Using the graphic feature selection method (SU-CFAM) with good speed and performance
Achieving 98.2% accuracy without using SU-CFAM, and 99.1% using it.
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