Award Number : DAMD 1798-1-8211 TITLE : Analysis of Interval Changes on Mammograms for Computer Aided Diagnosis

A new type of classifier combining an unsupervised have shown that the positive predictive value (i.e., ratio of the and a supervised model was designed and applied to classifinumber of breast cancers found to the total number of biopsies) cation of malignant and benign masses on mammograms. The is only 15% to 30% [2]-[4]. It is important to increase the unsupervised model was based on an adaptive resonance theory (ART2) network which clustered the masses into a number of positive predictive value without reducing the sensitivity of separate classes. The classes were divided into two types: one breast cancer detection. Computer-aided diagnosis (CAD) has containing only malignant masses and the other containing a mix the potential to increase the diagnostic accuracy by reducing of malignant and benign masses. The masses from the malignant the false-negative rate while increasing the positive predictive classes were classified by ART2. The masses from the mixed classes were input to a supervised linear discriminant classifier values of mammographic abnormalities. (LDA). In this way, some malignant masses were separated Classifier design is an important step in the development and classified by ART2 and the less distinguishable benign and of a CAD system. A classifier has to be able to merge malignant masses were classified by LDA. For the evaluation of the available input feature information and make a correct classifier performance, 348 regions of interest (RO's) containing evaluation. Commonly used classifiers for CAD include linear biopsy proven masses (169 benign and 179 malignant) were used. discriminants (LDA) [5], [6] and backpropagation neural netTen different partitions of training and test groups were randomly generated using an average of 73% of ROr's for training and works (BPN) [7]-[9] which have been shown to perform well 27% for testing. Classifier design, including feature selection and in lesion classification problems [10]-[22]. These classifiers weight optimization, was performed with the training group. are generally designed by supervised training. However, these The test group was kept independent of the training group. The types of classifiers have limitations dealing with the nonlinperformance of the hybrid classifier was compared to that of an LDA classifier alone and a backpropagation neural network eanties in the data (in case of LDA) and in generalizability (BPN). Receiver operating characteristics (ROC) analysis was when a limited number of training samples are available used to evaluate the accuracy of the classifiers. The average area (especially BPN). Another classification approach is based on under the ROC curve (A-) for the hybrid classifier was 0.81 as unsupervised classifiers, which cluster the data into different compared to 0.78 for the LDA and 0.80 for the BPN. The partial classes based on the similarities in the properties of the input 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, feature vectors. Therefore, unsupervised classifiers can be used These results indicate that the hybrid classifier is a promising to analyze the similarities within the data. However, it is approach for improving the accuracy of classification in CAD difficult to use them as a discriminatory classifier [29], [30]. applications. They also have limited generalizability when the training