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Research Article

Machine Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

  • Paper ID 702
  • Volume 3, Issue 5
  • Pages 57-60
  • Published Online: September-October 2022
Open Access Peer Reviewed

Authors

  • Dr.A.S.Shanthi1
  • R.Kokila2
  • D. Ananthi3
  • G. Indhumathi4
  • G. Shobika Sree Malya5
  • S R.Vahini6

123456 Department of Computer Science And Engineering, Tamilnadu College of Engineering, Tamilnadu, India.

Article Information

DOI
No DOI
Published Online
September-October 2022
Pages
57-60
Article Type
Research Article
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References

1. Liu J, Udupa JK, Odhner D, Hackney D, Moonis G (2005) A system for brain tumor volume estimation via MR imaging and fuzzy
connectedness. Comput Med Imaging Graphics 29(1):21–34
2. Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data.
IEEE Trans Med Imaging 17(1):87–97
3. Belaroussi B, Milles J, Carme S, Zhu YM, Benoit-Cattin H (2006) Intensity non-uniformity correction in MRI: existing methods and
their validation. Med Image Anal 10(2):234
4. Madabhushi A, Udupa JK (2005) Interplay between intensity standardization and inhomogeneity correction in MR image processing.
IEEE Trans Med Imaging 24(5):561– 57
5. Prastawa M, Bullitt E, Ho S, Gerig G (2004) A brain tumor segmentation framework based on outlier detection. Med Image Anal
8(3):275–283
6. PhillipsW, Velthuizen R, Phuphanich S, Hall L, Clarke L, SilbigerM(1995) Application of fuzzy c-means segmentation technique for
tissue differentiation inMR images of a hemorrhagic glioblastoma multiforme. Magn Reson Imaging 13(2):277–290
7. Clark MC, Hall LO, Goldgof DB, Velthuizen R, Murtagh FR, Silbiger MS (1998) Automatic tumor segmentation using knowledge
based techniques. IEEE Trans Med Imaging 17(2):187–201
8. Fletcher-Heath LM, Hall LO, Goldg of DB, Murtagh FR (2001) Automatic segmentation of non-enhancing brain tumors in magnetic
resonance images. ArtifIntell Med 21(1–3):43– 63
9. Warfield SK, Kaus M, Jolesz FA, Kikinis R (2000) Adaptive, template moderated, spatially varying statistical classification. Med
Image Anal 4(1):43–55
10. KausMR,Warfield SK, Nabavi A, Black PM, Jolesz FA, Kikinis R (2001) Automated segmentation of MR images of Brain Tumors1.
Radiology 218(2):586–591
11. Guillemaud R, Brady M (1997) Estimating the bias field of MR images. IEEE Trans Med Imaging 16(3):238–251
12. Corso JJ, Sharon E, Dube S, El-Saden S, Sinha U, Yuille A (2008) Efficient multilevel brain tumor segmentation with integrated
bayesian model classification. IEEE Trans Med Imaging 27(5):629–640
13. . Zhou J, Chan K, Chong V, Krishnan S (2006) Extraction of brain tumor from MR images using one-class support vector machine.
In: 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005. pp 6411–6414
14. Corso J, Yuille A, Sicotte N, Toga A (2007) Detection and segmentation of pathological structures by the extended graph-shifts
algorithm. In: Medical Image Computing and ComputerAssisted Intervention—MICCAI. pp 985–993
15. Schapire RE, Freund Y, Bartlett P, Lee WS (1998) Boosting the margin: a new explanation for the effectiveness of voting methods.
Ann Stat 26(5):1651–1686
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