Breast cancer tumor type recognition using graph feature selection technique and radial basis function neural network with optimal structure

• Published in: Journal of cancer research and therapeutics Vol. 14; no. 3; pp. 625 - 633
• Main Authors: Zarbakhsh, Payam, Addeh, Abdoljalil
• Format: Journal Article
• Language: English
• Published: India Wolters Kluwer India Pvt. Ltd 01.04.2018; Medknow Publications and Media Pvt. Ltd; Medknow Publications & Media Pvt. Ltd
• Subjects: Algorithms; Artificial neural networks; Breast cancer; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Care and treatment; Classification; Computer Simulation; Databases, Factual; Diagnosis; Diagnosis, Computer-Assisted - methods; Engineering; Female; Health aspects; Humans; International conferences; Mammography; Medical diagnosis; Medical prognosis; Models, Statistical; Neural networks; Neural Networks, Computer; Patient outcomes; Pattern recognition; Pattern Recognition, Automated; Prognosis; Womens health; Young women; Wisconsin; Wisconsin breast cancer database; Artificial bee colony; radial basis function neural network; feature selection; graph
• ISSN: 0973-1482; 1998-4138
• DOI: 10.4103/0973-1482.183561

Context: Breast cancer is a major cause of mortality in young women in the developing countries. Early diagnosis is the key to improve survival rate in cancer patients. Aims: In this paper an intelligent system is proposed to breast cancer tumor type recognition. Settings and Design: The proposed system includes three main module: The feature selection module, the classifier module and the optimization module. Feature selection plays an important role in pattern recognition systems. The better selection of features usually results in higher accuracy rate. Methods and Material: In the proposed system we used a new graph based feature selection approach to select the best features. In the classifier module, the radial basis function neural network (RBFNN)is used as classifier. In RBF training, the number of RBFs and their respective centers and widths (Spread) have very important role in its performance. Therefore, artificial bee colony (ABC) algorithm is proposed for selecting appropriate parameters of the classifier. Statistical Analysis Used: The RBFNN with optimal structure and the selected feature classified the tumors with 99.59% accuracy. Results: The proposed system is tested on Wisconsin breast cancer database (WBCD) and the simulation results show that the recommended system exhibits a high accuracy. Conclusions: The proposed system has a high recognition accuracy and therefore we recommend the proposed system for breast cancer tumor type recognition.