BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

1210 related articles for article (PubMed ID: 31128062)

  • 1. Deep Learning Based Skin Lesion Segmentation and Classification of Melanoma Using Support Vector Machine (SVM).
    R D S; A S
    Asian Pac J Cancer Prev; 2019 May; 20(5):1555-1561. PubMed ID: 31128062
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.
    Premaladha J; Ravichandran KS
    J Med Syst; 2016 Apr; 40(4):96. PubMed ID: 26872778
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
    Al-Masni MA; Al-Antari MA; Choi MT; Han SM; Kim TS
    Comput Methods Programs Biomed; 2018 Aug; 162():221-231. PubMed ID: 29903489
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.
    Pal A; Garain U; Chandra A; Chatterjee R; Senapati S
    Comput Methods Programs Biomed; 2018 Jun; 159():59-69. PubMed ID: 29650319
    [TBL] [Abstract][Full Text] [Related]  

  • 5. A novel cumulative level difference mean based GLDM and modified ABCD features ranked using eigenvector centrality approach for four skin lesion types classification.
    Wahba MA; Ashour AS; Guo Y; Napoleon SA; Elnaby MMA
    Comput Methods Programs Biomed; 2018 Oct; 165():163-174. PubMed ID: 30337071
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Efficient skin lesion segmentation using separable-Unet with stochastic weight averaging.
    Tang P; Liang Q; Yan X; Xiang S; Sun W; Zhang D; Coppola G
    Comput Methods Programs Biomed; 2019 Sep; 178():289-301. PubMed ID: 31416556
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Multiclass skin lesion localization and classification using deep learning based features fusion and selection framework for smart healthcare.
    Maqsood S; Damaševičius R
    Neural Netw; 2023 Mar; 160():238-258. PubMed ID: 36701878
    [TBL] [Abstract][Full Text] [Related]  

  • 8. A hierarchical three-step superpixels and deep learning framework for skin lesion classification.
    Afza F; Sharif M; Mittal M; Khan MA; Jude Hemanth D
    Methods; 2022 Jun; 202():88-102. PubMed ID: 33610692
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of Skin Cancer Using SVM, Random Forest and kNN Classifiers.
    Murugan A; Nair SAH; Kumar KPS
    J Med Syst; 2019 Jul; 43(8):269. PubMed ID: 31273532
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Integration of morphological preprocessing and fractal based feature extraction with recursive feature elimination for skin lesion types classification.
    Chatterjee S; Dey D; Munshi S
    Comput Methods Programs Biomed; 2019 Sep; 178():201-218. PubMed ID: 31416550
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Construction of saliency map and hybrid set of features for efficient segmentation and classification of skin lesion.
    Khan MA; Akram T; Sharif M; Saba T; Javed K; Lali IU; Tanik UJ; Rehman A
    Microsc Res Tech; 2019 Jun; 82(6):741-763. PubMed ID: 30768826
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Melanoma recognition in dermoscopy images using lesion's peripheral region information.
    Tajeddin NZ; Asl BM
    Comput Methods Programs Biomed; 2018 Sep; 163():143-153. PubMed ID: 30119849
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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; 12():127. PubMed ID: 22898352
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Skin lesion classification with ensembles of deep convolutional neural networks.
    Harangi B
    J Biomed Inform; 2018 Oct; 86():25-32. PubMed ID: 30103029
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Extraction of skin lesion texture features based on independent component analysis.
    Tabatabaie K; Esteki A; Toossi P
    Skin Res Technol; 2009 Nov; 15(4):433-9. PubMed ID: 19832954
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Segmentation of skin lesions in dermoscopy images using fuzzy classification of pixels and histogram thresholding.
    Garcia-Arroyo JL; Garcia-Zapirain B
    Comput Methods Programs Biomed; 2019 Jan; 168():11-19. PubMed ID: 30527129
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Automatic lesion border selection in dermoscopy images using morphology and color features.
    Mishra NK; Kaur R; Kasmi R; Hagerty JR; LeAnder R; Stanley RJ; Moss RH; Stoecker WV
    Skin Res Technol; 2019 Jul; 25(4):544-552. PubMed ID: 30868667
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features.
    Yu Z; Jiang X; Zhou F; Qin J; Ni D; Chen S; Lei B; Wang T
    IEEE Trans Biomed Eng; 2019 Apr; 66(4):1006-1016. PubMed ID: 30130171
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Developing a Recognition System for Diagnosing Melanoma Skin Lesions Using Artificial Intelligence Algorithms.
    Alsaade FW; Aldhyani THH; Al-Adhaileh MH
    Comput Math Methods Med; 2021; 2021():9998379. PubMed ID: 34055044
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Improving the diagnostic accuracy of dysplastic and melanoma lesions using the decision template combination method.
    Faal M; Miran Baygi MH; Kabir E
    Skin Res Technol; 2013 Feb; 19(1):e113-22. PubMed ID: 22672787
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 61.