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논문검색

The Use of Dual-Tree Complex Wavelet Transform (DTCWT) Based Feature for Mammogram Classification

초록

영어

Early detection of cancer is the best method to increase the chances of survival. In early stage, cancer can be detected using mammography, fine needle aspirate, and surgical biopsy. In this study, we propose the use of Dual-Tree Complex Wavelet Transform (DTCWT) based feature with neural network classifier for mammography image analysis. The result is evaluated using specificity, sensitivity, and accuracy. Computational experiments show the proposed method is superior compare to DWT with 96.3% accuracy.

목차

Abstract
 1. Introduction
 2. Material and Method
  2.1. Mammogram Database
  2.2. Classification Framework
 3. Result and Discussion
  3.1. Result
  3.2. Discussion
 4. Conclusion
 Acknowledgements
 References

저자정보

  • Lowis Magister Information Technology Department, Graduate Program, Bina Nusantara University. Jl Kebon Jeruk Raya No 27, Kebon Jeruk, Jakarta Barat
  • Hendra Magister Information Technology Department, Graduate Program, Bina Nusantara University. Jl Kebon Jeruk Raya No 27, Kebon Jeruk, Jakarta Barat
  • Lavinia Magister Information Technology Department, Graduate Program, Bina Nusantara University. Jl Kebon Jeruk Raya No 27, Kebon Jeruk, Jakarta Barat

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