IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Breast cancer detection using mammography
SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
Recognition of fatty liver using hybrid neural network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Using multiscale visual words for lung texture classification and retrieval
MCBR-CDS'11 Proceedings of the Second MICCAI international conference on Medical Content-Based Retrieval for Clinical Decision Support
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
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A modified self-organizing map with nonlinear weight adjustments has been applied to reduce the number of breast biopsies necessary for breast cancer diagnosis. Tissue features representing texture information from digital sonographic breast images were extracted from sonograms of benign and malignant breast tumors. The resulting hyperspace of data points was then used in a modified self-organizing map that objectively segments population distributions of lesions and accurately establishes benign and malignant regions. These methods were applied to a group of 102 problematic breast cases with sonographic images, including 34 with malignant lesions. All lesions were substantiated by excisional biopsy. The system can isolate clusters of purely benign lesions from other clusters containing both benign and malignant lesions. The hybrid neural network defined a region in which about 60% of the benign lesions were located exclusive of any malignant lesions. The experimental results also suggest that the modified self-organizing map provides more accurate population distribution maps than conventional Kohonen maps