Classification of malignant and benign masses based on hybrid ART2LDA approach

Author(s): Chan, H.P. | Hadjiiski, L. | Helvie, M. | Petrick, N. | Sahiner, B. |

Year: 1999

Citation: IEEE TRANSACTIONS ON MEDICAL IMAGING Volume: 18 Issue: 12 Pages: 1178-1187

Abstract: A new type of classifier combining an unsupervised and a supervised model was designed and applied to classification of malignant and benign masses on mammograms. The unsupervised model was based on an adaptive resonance theory (ART2) network which clustered the masses into a number of separate classes, The classes were divided into two types: one containing only malignant masses and the other containing a mix of malignant and benign masses, The masses from the malignant classes were classified by ART2, The masses from the mixed classes were input to a supervised linear discriminant classifier (LDA), In this way, some malignant masses were separated and classified by ART2 and the less distinguishable benign and malignant masses were classified by LDA, For the evaluation of classifier performance, 348 regions of interest (ROI s) containing biopsy proven masses (169 benign and 179 malignant) were used. Ten different partitions of training and test groups were randomly generated using an average of 73% of ROI s for training and 27% for testing. Classifier design, including feature selection and weight optimization, was performed with the training group. The test group was kept independent of the training group, The performance of the hybrid classifier was compared to that of an LDA classifier alone and a backpropagation neural network (BPN), Receiver operating characteristics (ROC) analysis was used to evaluate the accuracy of the classifiers, The average area under the ROC curve (A(2)) for the hybrid classifier was 0.81 as compared to 0.78 for the LDA and 0.80 for the BPN, The partial areas above a true positive fraction of 0.9 were 0.34, 0.27 and 0.31 for the hybrid, the LDA and the BPN classifier, respectively. These results indicate that the hybrid classifier is a promising approach for improving the accuracy of classification in CAD applications.

Topics: Machine Learning, Applications: Biological Classification, Medical Diagnosis, Models: ART 2 / Fuzzy ART, Modified ART,

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