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Journal Abstract Search

287 related items for PubMed ID: 31093789

  • 1. A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.
    Melekoodappattu JG, Subbian PS.
    J Med Syst; 2019 May 15; 43(7):183. PubMed ID: 31093789
    [Abstract] [Full Text] [Related]

  • 2. Computer aided detection system for micro calcifications in digital mammograms.
    Mohamed H, Mabrouk MS, Sharawy A.
    Comput Methods Programs Biomed; 2014 Oct 15; 116(3):226-35. PubMed ID: 24909786
    [Abstract] [Full Text] [Related]

  • 3. Deep feature-based automatic classification of mammograms.
    Arora R, Rai PK, Raman B.
    Med Biol Eng Comput; 2020 Jun 15; 58(6):1199-1211. PubMed ID: 32200453
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  • 4. Quantitative comparison of clustered microcalcifications in for-presentation and for-processing mammograms in full-field digital mammography.
    Wang J, Nishikawa RM, Yang Y.
    Med Phys; 2017 Jul 15; 44(7):3726-3738. PubMed ID: 28477395
    [Abstract] [Full Text] [Related]

  • 5. Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.
    Li F, Zhao C, Xia Z, Wang Y, Zhou X, Li GZ.
    BMC Complement Altern Med; 2012 Aug 16; 12():127. PubMed ID: 22898352
    [Abstract] [Full Text] [Related]

  • 6. Improving the accuracy in detection of clustered microcalcifications with a context-sensitive classification model.
    Wang J, Nishikawa RM, Yang Y.
    Med Phys; 2016 Jan 16; 43(1):159. PubMed ID: 26745908
    [Abstract] [Full Text] [Related]

  • 7. Automatic detection of abnormalities in mammograms.
    Suhail Z, Sarwar M, Murtaza K.
    BMC Med Imaging; 2015 Nov 06; 15():53. PubMed ID: 26545584
    [Abstract] [Full Text] [Related]

  • 8. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
    Wang Z, Xin J, Sun P, Lin Z, Yao Y, Gao X.
    Comput Methods Programs Biomed; 2018 Aug 06; 162():197-209. PubMed ID: 29903487
    [Abstract] [Full Text] [Related]

  • 9. Decision support system for breast cancer detection using mammograms.
    Ganesan K, Acharya RU, Chua CK, Min LC, Mathew B, Thomas AK.
    Proc Inst Mech Eng H; 2013 Jul 06; 227(7):721-32. PubMed ID: 23636749
    [Abstract] [Full Text] [Related]

  • 10. Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform.
    Jian W, Sun X, Luo S.
    Biomed Eng Online; 2012 Dec 19; 11():96. PubMed ID: 23253202
    [Abstract] [Full Text] [Related]

  • 11. Multi-scale textural feature extraction and particle swarm optimization based model selection for false positive reduction in mammography.
    Zyout I, Czajkowska J, Grzegorzek M.
    Comput Med Imaging Graph; 2015 Dec 19; 46 Pt 2():95-107. PubMed ID: 25795630
    [Abstract] [Full Text] [Related]

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  • 15. A comparison of methods for three-class mammograms classification.
    Milosevic M, Jovanovic Z, Jankovic D.
    Technol Health Care; 2017 Aug 09; 25(4):657-670. PubMed ID: 28436405
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  • 18. Framework of Computer Aided Diagnosis Systems for Cancer Classification Based on Medical Images.
    El Houby EMF.
    J Med Syst; 2018 Jul 11; 42(8):157. PubMed ID: 29995204
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  • 20. A novel approach for detection and classification of mammographic microcalcifications using wavelet analysis and extreme learning machine.
    Malar E, Kandaswamy A, Chakravarthy D, Giri Dharan A.
    Comput Biol Med; 2012 Sep 11; 42(9):898-905. PubMed ID: 22871899
    [Abstract] [Full Text] [Related]

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