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Journal of Cancer Therapy
Submission
Journal of Cancer Therapy
ISSN Print:
2151-1934
ISSN Online:
2151-1942
www.scirp.net/journal/jct
E-mail:
[email protected]
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"
Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection
"
written by
Edén A. Alanís-Reyes, José L. Hernández-Cruz, Jesús S. Cepeda, Camila Castro, Hugo Terashima-Marín, Santiago E. Conant-Pablos
,
published by
Journal of Cancer Therapy
,
Vol.3 No.6, 2012
has been cited by the following article(s):
Google Scholar
CrossRef
[1]
Overview of Cancer Management—The Role of Medical Imaging and Machine Learning Techniques in Early Detection of Cancer: Prospects, Challenges, and …
Open Access Library …
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[2]
Application of Soft Computing Techniques in The Diagnosis of Tropical Diseases: A Systematic Review
J. Trop. Dis. Public …
,
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[3]
Hybrid Subspace Fusion for Microcalcification Clusters Detection?
Journal of Fiber Bioengineering and Informatics
,
2015
[4]
Image Classification with Genetic Programming: Building a Stage 1 Computer Aided Detector for Breast Cancer
Handbook of Genetic Programming Applications
,
2015
[5]
乳房鈣化點偵測方法 GS-foveal algorithm
中山醫學大學醫學資訊學系碩士班學位論文
,
2015
[6]
Hybrid subspace fusion for microcalcification clusters detection
Journal of Fiber Bioengineering and Informatics
,
2015
[7]
Detection of tumour based on breast tissue categorization
2015
[8]
Pixel-based morphological technique for breast tumour detection
2015
[9]
Cluster analysis of breast cancer data using Genetic Algorithm and Spiking Neural Networks
2015
[10]
Data mining framework for breast cancer detection in mammograms: A hybrid feature extraction paradigm
Journal of Medical Imaging and Health Informatics
,
2014
[11]
Survey on Medical Data Cluster analysis using Feature Selection and Neural Networks
2014
[1]
Intelligent Data Engineering and Analytics
Smart Innovation, Systems and Technologies
,
2025
DOI:
10.1007/978-981-96-0139-4_2
[2]
Handbook of Genetic Programming Applications
2015
DOI:
10.1007/978-3-319-20883-1_10
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