Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques
217
Zitationen
2
Autoren
2001
Jahr
Abstract
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.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.849 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.418 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 12.005 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.348 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 8.114 Zit.