232 related articles for article (PubMed ID: 28582921)
21. An approach to the detection of lesions in mammograms using fuzzy image processing.
Bayram B; Acar U
J Int Med Res; 2007; 35(6):790-5. PubMed ID: 18034992
[TBL] [Abstract][Full Text] [Related]
22. 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]
23. Computer-aided diagnosis of contrast-enhanced spectral mammography: A feasibility study.
Patel BK; Ranjbar S; Wu T; Pockaj BA; Li J; Zhang N; Lobbes M; Zhang B; Mitchell JR
Eur J Radiol; 2018 Jan; 98():207-213. PubMed ID: 29279165
[TBL] [Abstract][Full Text] [Related]
24. SVM and neural networks comparison in mammographic CAD.
García-Orellana CJ; Gallardo-Caballero R; Macías-Macias M; González-Velasco H
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():3204-7. PubMed ID: 18002677
[TBL] [Abstract][Full Text] [Related]
25. Detection of mass regions in mammograms by bilateral analysis adapted to breast density using similarity indexes and convolutional neural networks.
Bandeira Diniz JO; Bandeira Diniz PH; Azevedo Valente TL; Corrêa Silva A; de Paiva AC; Gattass M
Comput Methods Programs Biomed; 2018 Mar; 156():191-207. PubMed ID: 29428071
[TBL] [Abstract][Full Text] [Related]
26. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks.
Bocchi L; Coppini G; Nori J; Valli G
Med Eng Phys; 2004 May; 26(4):303-12. PubMed ID: 15121055
[TBL] [Abstract][Full Text] [Related]
27. A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.
Sarvestani ZM; Jamali J; Taghizadeh M; Dindarloo MHF
J Cancer Res Clin Oncol; 2023 Aug; 149(9):6151-6170. PubMed ID: 36680580
[TBL] [Abstract][Full Text] [Related]
28. Learning from unbalanced data: a cascade-based approach for detecting clustered microcalcifications.
Bria A; Karssemeijer N; Tortorella F
Med Image Anal; 2014 Feb; 18(2):241-52. PubMed ID: 24292553
[TBL] [Abstract][Full Text] [Related]
29. Computer aided detection of clusters of microcalcifications on full field digital mammograms.
Ge J; Sahiner B; Hadjiiski LM; Chan HP; Wei J; Helvie MA; Zhou C
Med Phys; 2006 Aug; 33(8):2975-88. PubMed ID: 16964876
[TBL] [Abstract][Full Text] [Related]
30. Development of intelligent systems based on Bayesian regularization network and neuro-fuzzy models for mass detection in mammograms: A comparative analysis.
Mahersia H; Boulehmi H; Hamrouni K
Comput Methods Programs Biomed; 2016 Apr; 126():46-62. PubMed ID: 26831269
[TBL] [Abstract][Full Text] [Related]
31. SVM based system for classification of microcalcifications in digital mammograms.
Singh S; Kumar V; Verma HK; Singh D
Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():4747-50. PubMed ID: 17945853
[TBL] [Abstract][Full Text] [Related]
32. Microcalcification detection in full-field digital mammograms: A fully automated computer-aided system.
Basile TMA; Fanizzi A; Losurdo L; Bellotti R; Bottigli U; Dentamaro R; Didonna V; Fausto A; Massafra R; Moschetta M; Tamborra P; Tangaro S; La Forgia D
Phys Med; 2019 Aug; 64():1-9. PubMed ID: 31515007
[TBL] [Abstract][Full Text] [Related]
33. Segmentation for the enhancement of microcalcifications in digital mammograms.
Milosevic M; Jankovic D; Peulic A
Technol Health Care; 2014; 22(5):701-15. PubMed ID: 25059254
[TBL] [Abstract][Full Text] [Related]
34. Characterizing Architectural Distortion in Mammograms by Linear Saliency.
Narváez F; Alvarez J; Garcia-Arteaga JD; Tarquino J; Romero E
J Med Syst; 2017 Feb; 41(2):26. PubMed ID: 28005248
[TBL] [Abstract][Full Text] [Related]
35. Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms.
Kashyap KL; Bajpai MK; Khanna P
Comput Biol Med; 2017 Aug; 87():22-37. PubMed ID: 28549292
[TBL] [Abstract][Full Text] [Related]
36. A new approach to the detection of lesions in mammography using fuzzy clustering.
Wang Y; Shi H; Ma S
J Int Med Res; 2011; 39(6):2256-63. PubMed ID: 22289541
[TBL] [Abstract][Full Text] [Related]
37. Convolutional Neural Networks for the Segmentation of Microcalcification in Mammography Imaging.
Valvano G; Santini G; Martini N; Ripoli A; Iacconi C; Chiappino D; Della Latta D
J Healthc Eng; 2019; 2019():9360941. PubMed ID: 31093321
[TBL] [Abstract][Full Text] [Related]
38. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications.
Wei L; Yang Y; Nishikawa RM; Jiang Y
IEEE Trans Med Imaging; 2005 Mar; 24(3):371-80. PubMed ID: 15754987
[TBL] [Abstract][Full Text] [Related]
39. [Computer aided diagnosis of calcifications in mammograms].
Li G; Liu H; Cheng J
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2011 Feb; 28(1):170-4. PubMed ID: 21485207
[TBL] [Abstract][Full Text] [Related]
40. Breast cancer detection and classification in digital mammography based on Non-Subsampled Contourlet Transform (NSCT) and Super Resolution.
Pak F; Kanan HR; Alikhassi A
Comput Methods Programs Biomed; 2015 Nov; 122(2):89-107. PubMed ID: 26206406
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
