260 related articles for article (PubMed ID: 34302269)
1. An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features.
Aswathy MA; Jagannath M
Med Biol Eng Comput; 2021 Sep; 59(9):1773-1783. PubMed ID: 34302269
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Computer assisted recognition of breast cancer in biopsy images via fusion of nucleus-guided deep convolutional features.
George K; Sankaran P; K PJ
Comput Methods Programs Biomed; 2020 Oct; 194():105531. PubMed ID: 32422473
[TBL] [Abstract][Full Text] [Related]
4. Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion.
George K; Faziludeen S; Sankaran P; Joseph K P
Comput Biol Med; 2020 Sep; 124():103954. PubMed ID: 32777599
[TBL] [Abstract][Full Text] [Related]
5. Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers.
Mavroforakis ME; Georgiou HV; Dimitropoulos N; Cavouras D; Theodoridis S
Artif Intell Med; 2006 Jun; 37(2):145-62. PubMed ID: 16716579
[TBL] [Abstract][Full Text] [Related]
6. A support vector machine classifier reduces interscanner variation in the HRCT classification of regional disease pattern in diffuse lung disease: comparison to a Bayesian classifier.
Chang Y; Lim J; Kim N; Seo JB; Lynch DA
Med Phys; 2013 May; 40(5):051912. PubMed ID: 23635282
[TBL] [Abstract][Full Text] [Related]
7. Automated Identification of Hookahs (Waterpipes) on Instagram: An Application in Feature Extraction Using Convolutional Neural Network and Support Vector Machine Classification.
Zhang Y; Allem JP; Unger JB; Boley Cruz T
J Med Internet Res; 2018 Nov; 20(11):e10513. PubMed ID: 30452385
[TBL] [Abstract][Full Text] [Related]
8. Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images.
Ferjaoui R; Cherni MA; Boujnah S; Kraiem NEH; Kraiem T
Comput Methods Programs Biomed; 2021 Sep; 209():106320. PubMed ID: 34390938
[TBL] [Abstract][Full Text] [Related]
9. Machine learning-based classification of the movements of children with profound or severe intellectual or multiple disabilities using environment data features.
Herbuela VRDM; Karita T; Furukawa Y; Wada Y; Toya A; Senba S; Onishi E; Saeki T
PLoS One; 2022; 17(6):e0269472. PubMed ID: 35771797
[TBL] [Abstract][Full Text] [Related]
10. An expert support system for breast cancer diagnosis using color wavelet features.
Issac Niwas S; Palanisamy P; Chibbar R; Zhang WJ
J Med Syst; 2012 Oct; 36(5):3091-102. PubMed ID: 22005900
[TBL] [Abstract][Full Text] [Related]
11. Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.
Paiva JS; Cardoso J; Pereira T
Int J Med Inform; 2018 Jan; 109():30-38. PubMed ID: 29195703
[TBL] [Abstract][Full Text] [Related]
12. A comparison of methods for three-class mammograms classification.
Milosevic M; Jovanovic Z; Jankovic D
Technol Health Care; 2017 Aug; 25(4):657-670. PubMed ID: 28436405
[TBL] [Abstract][Full Text] [Related]
13. SVM classifier of cervical histopathology images based on texture and morphological features.
He S; Xiao B; Wei H; Huang S; Chen T
Technol Health Care; 2023; 31(1):69-80. PubMed ID: 35754238
[TBL] [Abstract][Full Text] [Related]
14. A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images.
Wei M; Du Y; Wu X; Su Q; Zhu J; Zheng L; Lv G; Zhuang J
Comput Math Methods Med; 2020; 2020():5894010. PubMed ID: 33062038
[TBL] [Abstract][Full Text] [Related]
15. A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features.
Huang H; Feng X; Zhou S; Jiang J; Chen H; Li Y; Li C
BMC Bioinformatics; 2019 Jun; 20(Suppl 8):290. PubMed ID: 31182028
[TBL] [Abstract][Full Text] [Related]
16. Automated classification of nasal polyps in endoscopy video-frames using handcrafted and CNN features.
Ay B; Turker C; Emre E; Ay K; Aydin G
Comput Biol Med; 2022 Aug; 147():105725. PubMed ID: 35716434
[TBL] [Abstract][Full Text] [Related]
17. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery.
Thanh Noi P; Kappas M
Sensors (Basel); 2017 Dec; 18(1):. PubMed ID: 29271909
[TBL] [Abstract][Full Text] [Related]
18. Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification.
Lee HS; Hong H; Jung DC; Park S; Kim J
Med Phys; 2017 Jul; 44(7):3604-3614. PubMed ID: 28376281
[TBL] [Abstract][Full Text] [Related]
19. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight.
Sharma S; Mehra R
J Digit Imaging; 2020 Jun; 33(3):632-654. PubMed ID: 31900812
[TBL] [Abstract][Full Text] [Related]
20. Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.
Khan MM; Tazin T; Zunaid Hussain M; Mostakim M; Rehman T; Singh S; Gupta V; Alomeir O
Comput Intell Neurosci; 2022; 2022():6333573. PubMed ID: 35712068
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
