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  1. Medical and Biological Engineering and Computing
  2. Medical and Biological Engineering and Computing : Volume 40
  3. Medical and Biological Engineering and Computing : Volume 40, Issue 2, March 2002
  4. Fuzzy rules to predict degree of malignancy in brain glioma
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Medical and Biological Engineering and Computing : Volume 55
Medical and Biological Engineering and Computing : Volume 54
Medical and Biological Engineering and Computing : Volume 53
Medical and Biological Engineering and Computing : Volume 52
Medical and Biological Engineering and Computing : Volume 51
Medical and Biological Engineering and Computing : Volume 50
Medical and Biological Engineering and Computing : Volume 49
Medical and Biological Engineering and Computing : Volume 48
Medical and Biological Engineering and Computing : Volume 47
Medical and Biological Engineering and Computing : Volume 46
Medical and Biological Engineering and Computing : Volume 45
Medical and Biological Engineering and Computing : Volume 44
Medical and Biological Engineering and Computing : Volume 43
Medical and Biological Engineering and Computing : Volume 42
Medical and Biological Engineering and Computing : Volume 41
Medical and Biological Engineering and Computing : Volume 40
Medical and Biological Engineering and Computing : Volume 40, Issue 6, November 2002
Medical and Biological Engineering and Computing : Volume 40, Issue 5, September 2002
Medical and Biological Engineering and Computing : Volume 40, Issue 4, July 2002
Medical and Biological Engineering and Computing : Volume 40, Issue 3, May 2002
Medical and Biological Engineering and Computing : Volume 40, Issue 2, March 2002
Fuzzy rules to predict degree of malignancy in brain glioma
Model-based technique for the measurement of skin thickness in mammography
Novel ultrasonic fusion imaging method based on cyclic variation in myocardial backscatter
Ultrasound image matching using genetic algorithms
Ambulatory measurement of upper limb usage and mobility-related activities during normal daily life with an upper limb-activity monitor: A feasibility study
Biomechanical comparison of isokinetic lifting and free lifting when applied to chronic low back pain rehabilitation
Dynamic simulation of the natural and replaced human ankle joint
Root canal length measurement in teeth with electrolyte compensation
Estimation of pulmonary arterial pressure by a neural network analysis using features based on time-frequency representations of the second heart sound
Detection of atrial-flutter and atrial-fibrillation waveforms by fetal magnetocardiogram
Experimental and numerical study of the colour appearance of tattoo models
Analysis of cardiac left-ventricular volume based on time warping averaging
Non-invasive Wedensky modulation within the QRS complex
Joint symbolic dynamic analysis of beat-to-beat interactions of heart rate and systolic blood pressure in normal pregnancy
Unconstrained and non-invasive measurement of heart-beat and respiration periods using a phonocardiographic sensor
Motor unit conduction velocity distribution estimation: Assessment of two short-term processing methods
Effect of signal length on the performance of independent component analysis when extracting the lambda wave
Medical and Biological Engineering and Computing : Volume 40, Issue 1, January 2002
Medical and Biological Engineering and Computing : Volume 39
Medical and Biological Engineering and Computing : Volume 38
Medical and Biological Engineering and Computing : Volume 37
Medical and Biological Engineering and Computing : Volume 36
Medical and Biological Engineering and Computing : Volume 35

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Editorial: Bioengineering publications

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Fuzzy rules to predict degree of malignancy in brain glioma

Content Provider SpringerLink
Author Ye, C. Z. Yang, J. Geng, D. Y. Zhou, Y. Chen, N. Y.
Copyright Year 2002
Abstract The current pre-operative assessment of the degree of malignancy in brain glioma is based on magnetic resonance imaging (MRI) findings and clinical data. 280 cases were studied, of which 111 were high-grade malignancies and 169 were low-grade, so that regular and interpretable patterns of the relationships between glioma MRI features and the degree of malignancy could be acquired. However, as uncertainties in the data and missing values existed, a fuzzy rule extraction algorithm based on a fuzzy min-max neural network (FMMNN) was used. The performance of a multi-layer perceptron network (MLP) trained with the error back-propagation algorithm (BP), the decision tree algorithm ID3, nearest neighbour and the original fuzzy min-max neural network were also evaluated. The results showed that two fuzzy decision rules on only six features achieved an accuracy of 84.6% (89.9% for low-grade and 76.6% for high-grade cases). Investigations with the proposed algorithm revealed that age, mass effect, oedema, post-contrast enhancement, blood supply, calcification, haemorrhage and the signal intensity of the T1-weighted image were important diagnostic factors.
Starting Page 145
Ending Page 152
Page Count 8
File Format PDF
ISSN 01400118
Journal Medical and Biological Engineering and Computing
Volume Number 40
Issue Number 2
e-ISSN 17410444
Language English
Publisher Springer-Verlag
Publisher Date 2002-01-01
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Brain glioma Classification Fuzzy rule extraction MRI Human Physiology Neurosciences Imaging Radiology Computer Applications Biomedical Engineering
Content Type Text
Resource Type Article
Subject Biomedical Engineering Computer Science Applications
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