BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

203 related articles for article (PubMed ID: 31450903)

  • 1. Comparison Analysis of Linear Discriminant Analysis and Cuckoo-Search Algorithm in the Classification of Breast Cancer from Digital Mammograms.
    S R SC; Rajaguru H
    Asian Pac J Cancer Prev; 2019 Aug; 20(8):2333-2337. PubMed ID: 31450903
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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; 227(7):721-32. PubMed ID: 23636749
    [TBL] [Abstract][Full Text] [Related]  

  • 3. An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier.
    Singh SP; Urooj S
    J Med Syst; 2016 Apr; 40(4):105. PubMed ID: 26892455
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Breast cancer diagnosis in digitized mammograms using curvelet moments.
    Dhahbi S; Barhoumi W; Zagrouba E
    Comput Biol Med; 2015 Sep; 64():79-90. PubMed ID: 26151831
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Aiding the Digital Mammogram for Detecting the Breast Cancer Using Shearlet Transform and Neural Network.
    P S; R T
    Asian Pac J Cancer Prev; 2018 Sep; 19(9):2665-2671. PubMed ID: 30256567
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms.
    Rahmani Seryasat O; Haddadnia J
    Clin Breast Cancer; 2018 Jun; 18(3):e407-e420. PubMed ID: 29141776
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.
    Chan HP; Sahiner B; Lam KL; Petrick N; Helvie MA; Goodsitt MM; Adler DD
    Med Phys; 1998 Oct; 25(10):2007-19. PubMed ID: 9800710
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Mammographic image based breast tissue classification with kernel self-optimized fisher discriminant for breast cancer diagnosis.
    Li JB
    J Med Syst; 2012 Aug; 36(4):2235-44. PubMed ID: 21476083
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A swarm optimized neural network system for classification of microcalcification in mammograms.
    Dheeba J; Selvi ST
    J Med Syst; 2012 Oct; 36(5):3051-61. PubMed ID: 21947904
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines.
    Jebamony J; Jacob D
    Curr Med Imaging; 2020; 16(6):703-710. PubMed ID: 32723242
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Automated diagnosis of mammogram images of breast cancer using discrete wavelet transform and spherical wavelet transform features: a comparative study.
    Ganesan K; Acharya UR; Chua CK; Min LC; Abraham TK
    Technol Cancer Res Treat; 2014 Dec; 13(6):605-15. PubMed ID: 24000991
    [TBL] [Abstract][Full Text] [Related]  

  • 12. An improved decision support system for detection of lesions in mammograms using Differential Evolution Optimized Wavelet Neural Network.
    Dheeba J; Tamil Selvi S
    J Med Syst; 2012 Oct; 36(5):3223-32. PubMed ID: 22173907
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An Efficient Approach for Automated Mass Segmentation and Classification in Mammograms.
    Dong M; Lu X; Ma Y; Guo Y; Ma Y; Wang K
    J Digit Imaging; 2015 Oct; 28(5):613-25. PubMed ID: 25776767
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Ant-cuckoo colony optimization for feature selection in digital mammogram.
    Jona JB; Nagaveni N
    Pak J Biol Sci; 2014 Jan; 17(2):266-71. PubMed ID: 24783812
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Breast density classification to reduce false positives in CADe systems.
    Vállez N; Bueno G; Déniz O; Dorado J; Seoane JA; Pazos A; Pastor C
    Comput Methods Programs Biomed; 2014 Feb; 113(2):569-84. PubMed ID: 24286729
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Fusion of k-Gabor features from medio-lateral-oblique and craniocaudal view mammograms for improved breast cancer diagnosis.
    Sasikala S; Ezhilarasi M
    J Cancer Res Ther; 2018; 14(5):1036-1041. PubMed ID: 30197344
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Computer-aided detection systems for breast masses: comparison of performances on full-field digital mammograms and digitized screen-film mammograms.
    Wei J; Hadjiiski LM; Sahiner B; Chan HP; Ge J; Roubidoux MA; Helvie MA; Zhou C; Wu YT; Paramagul C; Zhang Y
    Acad Radiol; 2007 Jun; 14(6):659-69. PubMed ID: 17502255
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Bilateral analysis based false positive reduction for computer-aided mass detection.
    Wu YT; Wei J; Hadjiiski LM; Sahiner B; Zhou C; Ge J; Shi J; Zhang Y; Chan HP
    Med Phys; 2007 Aug; 34(8):3334-44. PubMed ID: 17879797
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms.
    Pawar MM; Talbar SN; Dudhane A
    J Healthc Eng; 2018; 2018():5940436. PubMed ID: 30356422
    [TBL] [Abstract][Full Text] [Related]  

  • 20. False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis.
    Wei D; Chan HP; Petrick N; Sahiner B; Helvie MA; Adler DD; Goodsitt MM
    Med Phys; 1997 Jun; 24(6):903-14. PubMed ID: 9198026
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 11.