Detection and classification of masses in mammographic images in a multi-kernel approach

@article{Lima2016DetectionAC,
  title={Detection and classification of masses in mammographic images in a multi-kernel approach},
  author={Sidney M. L. Lima and Abel G. da Silva Filho and W. Santos},
  journal={ArXiv},
  year={2016},
  volume={abs/1712.07116}
}
  • Sidney M. L. Lima, Abel G. da Silva Filho, W. Santos
  • Published 2016
  • Computer Science, Engineering
  • ArXiv
  • We propose a method to detect and classify mammographic lesions using the regions of interest of images.We use multi-resolution wavelets and Zernike moments as extract feature extractor image stage.We can combine both texture and shape features, which can be applied both to the detection and classification of mammary lesions.Considering the ratio between accuracy and training time, our proposal proved to be 50 times superior to state-of-the-art approaches.As our proposed model can combine high… CONTINUE READING
    Involvement of Machine Learning for Breast Cancer Image Classification: A Survey
    60

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 35 REFERENCES
    Classification of masses in mammographic image using wavelet domain features and polynomial classifier
    63
    Classification of benign and malignant masses based on Zernike moments
    190
    Benign and malignant breast tumors classification based on region growing and CNN segmentation
    190
    Breast tumor detection in digital mammography based on extreme learning machine
    61
    Fast opposite weight learning rules with application in breast cancer diagnosis
    52
    Evolutionary Fuzzy Extreme Learning Machine for Mammographic Risk Analysis
    49