Investigating feedforward neural networks with respect to the rejection of spurious patterns
Pattern Recognition Letters
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
To reject or not to reject: that is the question-an answer in caseof neural classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiresolution detection of spiculated lesions in digital mammograms
IEEE Transactions on Image Processing
Development of tolerant features for characterization of masses in mammograms
Computers in Biology and Medicine
Expert Systems with Applications: An International Journal
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An improved GVF snake based breast region extrapolation scheme for digital mammograms
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Mammogram retrieval on similar mass lesions
Computer Methods and Programs in Biomedicine
Pixel-based machine learning in medical imaging
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Computers & Mathematics with Applications
Ensemble Classifier for Benign-Malignant Mass Classification
International Journal of Computer Vision and Image Processing
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Computerized methods have recently shown a great potential in providing radiologists with a second opinion about the visual diagnosis of the malignancy of mammographic masses. The computer-aided diagnosis (CAD) system we developed for the mass characterization is mainly based on a segmentation algorithm and on the neural classification of several features computed on the segmented mass. Mass-segmentation plays a key role in most computerized systems. Our technique is a gradient-based one, showing the main characteristic that no free parameters have been evaluated on the data set used in this analysis, thus it can directly be applied to data sets acquired in different conditions without any ad hoc modification. A data set of 226 masses (109 malignant and 117 benign) has been used in this study. The segmentation algorithm works with a comparable efficiency both on malignant and benign masses. Sixteen features based on shape, size and intensity of the segmented masses are extracted and analyzed by a multi-layered perceptron neural network trained with the error back-propagation algorithm. The capability of the system in discriminating malignant from benign masses has been evaluated in terms of the receiver-operating characteristic (ROC) analysis. A feature selection procedure has been carried out on the basis of the feature discriminating power and of the linear correlations interplaying among them. The comparison of the areas under the ROC curves obtained by varying the number of features to be classified has shown that 12 selected features out of the 16 computed ones are powerful enough to achieve the best classifier performances. The radiologist assigned the segmented masses to three different categories: correctly-, acceptably- and non-acceptably-segmented masses. We initially estimated the area under ROC curve only on the first category of segmented masses (the 88.5% of the data set), then extending the classification to the second subclass (reaching the 97.8% of the data set) and finally to the whole data set, obtaining Az=0.805±0.030, 0.787±0.024 and 0.780±0.023, respectively.