A comparison of deep learning and hand crafted features in medical image modality classification

@article{Khan2016ACO,
  title={A comparison of deep learning and hand crafted features in medical image modality classification},
  author={Sameer Khan and Suet-Peng Yong},
  journal={2016 3rd International Conference on Computer and Information Sciences (ICCOINS)},
  year={2016},
  pages={633-638}
}
Modality corresponding to medical images is a vital filter in medical image retrieval systems, as radiologists or physicians are interested in only one of radiology images e.g CT scan, MRI, X-ray. Various handcrafted feature schemes have been proposed for medical image modality classification. On the other hand not enough attempts have been made for deep learned feature extraction. A comparative evaluation of both handcrafted and deep learned features for medical image modality classification… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 22 REFERENCES

Ensemble classification with modified SIFT descriptor for medical image modality

  • 2015 International Conference on Image and Vision Computing New Zealand (IVCNZ)
  • 2015
VIEW 12 EXCERPTS

Object recognition from local scale-invariant features

  • Proceedings of the Seventh IEEE International Conference on Computer Vision
  • 1999
VIEW 11 EXCERPTS
HIGHLY INFLUENTIAL

Convolutional neural networks for mammography mass lesion classification

John Arevalo, Fabio A Gonzalez, Raul Ramos-Pollan, Jose L Oliveira, Miguel Angel Guevara Lopez
  • In Engineering in Medicine and Biology Society (EMBC),
  • 2015
VIEW 1 EXCERPT