ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 4
Improved spiking neural networks for EEG classification and epilepsy and seizure detection
Integrated Computer-Aided Engineering
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Integrated Computer-Aided Engineering
Expert Systems with Applications: An International Journal
Enhanced probabilistic neural network with local decision circles: A robust classifier
Integrated Computer-Aided Engineering
Computer theory and digital image processing applied to brain activation recognition
Integrated Computer-Aided Engineering
ECG - QRS detection method adopting wavelet parallel filter banks
WAMUS'07 Proceedings of the 7th WSEAS international conference on Wavelet analysis & multirate systems
Breast Cancer Diagnosis: Analyzing Texture of Tissue Surrounding Microcalcifications
IEEE Transactions on Information Technology in Biomedicine
Identification of anatomic retinal structures for macular delineation in fluorescein angiograms
Integrated Computer-Aided Engineering
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In this paper, a Computer Aided System for microcalcification cluster diagnosis in mammographic images is presented. The method is characterized by three phases. In fact, single microcalcifications are first localized then microcalcifications having a cluster pattern are detected. In the last step the procedure classifies the abnormalities as benign or malignant microcalcification clusters. Features extracted from localized single microcalcifications are fed into a Support Vector Machine classifier to verify the presence of microcalcification cluster, minimizing false positive detections. For the diagnosis purpose, an Artificial Neural Network classifier is implemented which makes use of features extracted from previously detected microcalcification clusters as inputs. The performance of the implemented system is evaluated taking into account the accuracy of both detecting and classifying microcalcification clusters. Adopting the MIAS database as test bench, a sensitivity of about 98.4 at a rate of 0.85 FP/image is achieved in detecting microcalcification clusters. Moreover, the method gets a sensitivity of about 93.5% and an accuracy value equal to 94.2% in classifying the detected microcalcification clusters. The obtained system performance shows its ability of aiding the interpretation of specialists and, consequently, it could be considered as a "second opinion" method.