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Artificial Intelligence Approach to Breast Cancer Classification

Abstract Details

2009, Master of Science in Engineering, University of Akron, Biomedical Engineering.
Breast cancer is the second most common form of cancer amongst females and also the fifth most cause of cancer deaths worldwide. In case of this particular type of malignancy, early detection is the best form of cure and hence timely and accurate diagnosis of the tumor is extremely vital. Extensive research has been carried out on automating the critical diagnosis procedure as various machine learning algorithms and software tools have been deployed to aid physicians in optimizing the decision task effectively. In this research, we present a novel matrix of an artificial neural network system to effectively classify breast cancer tumors as either malignant or benign. This classification system makes use of both clinical as well as genetic data. Artificial neural networks of different architectures are incorporated in the system to classify both the image based clinical dataset as well as the microarray dataset derived from blood cells. Both the datasets are subjected to individual analysis to compute the optimum number of input features to the neural network matrix. Randomly selected sample instances from both the clinical and microarray original datasets then serve as an input to the Dempster-Shafer theory of evidence block where the outputs are fused to provide a final diagnostic assessment and compared with the neural network analysis.The results indicate that the fused output of the Dempster-Shafer block significantly outperform the individual classifier’s outputs.
Zhong-Hui Duan (Advisor)
Dale Mugler (Advisor)
107 p.

Recommended Citations

Citations

  • Vaidya, P. S. (2009). Artificial Intelligence Approach to Breast Cancer Classification [Master's thesis, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599

    APA Style (7th edition)

  • Vaidya, Priyanka. Artificial Intelligence Approach to Breast Cancer Classification. 2009. University of Akron, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599.

    MLA Style (8th edition)

  • Vaidya, Priyanka. "Artificial Intelligence Approach to Breast Cancer Classification." Master's thesis, University of Akron, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1240957599

    Chicago Manual of Style (17th edition)