Journal article
Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates
Cancer letters, Vol.77(2), pp.163-171
03/15/1994
DOI: 10.1016/0304-3835(94)90099-X
PMID: 8168063
Abstract
An interactive computer system evaluates and diagnoses based on cytologic features derived directly from a digital scan of fine-needle aspirate (FNA) slides. A consecutive series of 569 patients provided the data to develop the system and an additional 54 consecutive, new patients provided samples to test the system. The projected prospective accuracy of the system estimated by tenfold cross validation was 97%. The actual accuracy on 54 new samples (36 benign, 1 atypia, and 17 malignant) was 100%. Digital image analysis coupled with machine learning techniques will improve diagnostic accuracy of breast fine needle aspirates.
Details
- Title: Subtitle
- Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates
- Creators
- William H. Wolberg - University of Wisconsin–MadisonW.Nick Street - University of Wisconsin–MadisonO.L. Mangasarian - University of Wisconsin–Madison
- Resource Type
- Journal article
- Publication Details
- Cancer letters, Vol.77(2), pp.163-171
- Publisher
- Elsevier Ireland Ltd
- DOI
- 10.1016/0304-3835(94)90099-X
- PMID
- 8168063
- ISSN
- 0304-3835
- eISSN
- 1872-7980
- Language
- English
- Date published
- 03/15/1994
- Academic Unit
- Bus Admin College; Nursing; Computer Science; Business Analytics
- Record Identifier
- 9984380514102771
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