A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques.
An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing microcalcifications' 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 to classify it into benign/malignant. The system was developed on a 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.
Verma
B
B
School of Information Technology, Griffith University, Gold Coast, Queens, Australia.
Zakos
J
J
D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D003936:Diagnosis, Computer-Assisted; D005260:Female; D017143:Fuzzy Logic; D006801:Humans; D008327:Mammography / Q000379:methods*