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A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques

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2 Author(s)
Verma, B. ; Sch. of Inf. Technol., Griffith Univ., Brisbane, Qld., Australia ; Zakos, J.

An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcification patterns earlier and faster than typical screening programs. In this paper, we present a system based on fuzzy-neural and feature extraction techniques for detecting and diagnosing microcalcifications' patterns in digital mammograms. We have investigated and analyzed a number of feature extraction techniques and found that a combination of three features (such as entropy, standard deviation and number of pixels) is the best combination to distinguish a benign microcalcification pattern from one that is malignant. A fuzzy technique in conjunction with three features was used to detect a microcalcification pattern and a neural network was used to classify it into benign/malignant. The system was developed on a Microsoft Windows platform. It is an easy-to-use intelligent system that gives the user options to diagnose, detect, enlarge, zoom and measure distances of areas in digital mammograms.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:5 ,  Issue: 1 )