Abstract
This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.
| Original language | English |
|---|---|
| Pages (from-to) | 316-319 |
| Number of pages | 4 |
| Journal | Lecture Notes in Computer Science |
| Volume | 3614 |
| Issue number | PART II |
| DOIs | |
| Publication status | Published - 2005 |
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A hybrid classifier for mass classification with different kinds of features in mammography. / Zhang, Ping; Kumar, Kuldeep; Verma, Brijesh.
In: Lecture Notes in Computer Science, Vol. 3614, No. PART II, 2005, p. 316-319.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - A hybrid classifier for mass classification with different kinds of features in mammography
AU - Zhang, Ping
AU - Kumar, Kuldeep
AU - Verma, Brijesh
PY - 2005
Y1 - 2005
N2 - This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.
AB - This paper proposes a hybrid system which combines computer extracted features and human interpreted features from the mammogram, with the statistical classifier's output as another kind of features in conjunction with a genetic neural network classifier. The hybrid system produced better results than the single statistical classifier and neural network. The highest classification rate reached 91.3%. The area value under the ROC curve is 0.962. The results indicated that the mixed features contribute greatly for the classification of mass patterns into benign and malignant.
UR - http://www.scopus.com/inward/record.url?scp=26944500190&partnerID=8YFLogxK
U2 - 10.1007/11540007_38
DO - 10.1007/11540007_38
M3 - Article
VL - 3614
SP - 316
EP - 319
JO - Lecture Notes in Computer Science
JF - Lecture Notes in Computer Science
SN - 0302-9743
IS - PART II
ER -