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Table 1 Indicator Benign Malignant

in Use of a Multi-Layer Perceptron to Predict Malignancy in Ovarian Tumors
by H. Verrelst, Y. Moreau, J. Vandewalle, Ewalle J, D. Timmerman, Yves Moreau, Joos V, Dirk Timmerman
"... In PAGE 3: ... 2 Data acquisition and feature selection The data were derived from a study group of 191 consecutive patients who were referred to a single institution (University Hospitals Leuven, Belgium) from August 1994 to August 1996. Table1 lists the di erent indicators which were considered, together with their mean value and standard deviations or together with the relative presence in cases of benign and malignant tumors. Table 1 Indicator Benign Malignant... In PAGE 3: ... Every tumor was extensively examined for its morphologic characteristics. Table1 lists the selected morphologic features: presence of ab- dominal uid collection, papillary structures ( gt; 3mm), smooth internal walls, wall irregularities, whether the cysts were unilocular, multilocular-solid and/or present on both pelvic sides. All outcomes are binary valued: every observation relates to the presence (1) or absence (0) of these characteristics.... ..."

(Table 4). Table 4. US and MRI quantitative variables reflecting differences between different groups of lesions (II, III). Mean values and 95% confidence intervals.

in Reviewed by
by Solid Breast Lesions, Docent Tarja Rissanen, Docent Eija Pääkkö, Docent Martti Pamilo 2003
"... In PAGE 48: ...esions, p lt; 0.05. **Statistically significant difference between malignant and other benign lesions, p lt; 0.05. US = ultrasonography, MRI = magnetic resonance imaging, ROI = region of interest. The analysis of the slope of the US time-signal intensity curve and the area under the curve (Study III) revealed no statistically significant differences between any of the diagnostic groups (Table4 ). The mean time to peak enhancement was 29 seconds in ... In PAGE 50: ... The slope of the MR imaging time- signal intensity curve (with both ROIs) was able to differentiate malignant from benign lesions other than fibroadenomas. Likewise, using the area under the curve and the relative enhancement, a statistically significant difference was found between malignant and benign lesions except fibroadenomas (Table4 ). When compared to biopsy, the sensitivity, specificity and accuracy was 100%, 82% and 93% at low field compared to 100%, 73% and 89% at high field, respectively (Study IV) (Table 6).... ..."

Table 3. Twelve features used for the classification benign/malignant clusters.

in unknown title
by unknown authors 1998

Table 8. Classification Of Test Data By PNN Class Benign Malignant True

in BREAST CANCER DIAGNOSIS USING STATISTICAL NEURAL NETWORKS
by Tüba Kiyan, Tülay Yildirim

Table 10. Classification Of Test Data By MLP (Average) Class Benign Malignant

in BREAST CANCER DIAGNOSIS USING STATISTICAL NEURAL NETWORKS
by Tüba Kiyan, Tülay Yildirim

TABLE 2 Performance Values of Tpeak for Differentiation of Benign from Malignant Tumors

in Index terms:
by Hidetake Yabuuchi Md, Tatsuro Fukuya Md, Tsuyoshi Tajima Md, Yoichi Hachitanda Md, Kichinobu Tomita Md, Mitsuru Koga Md 2002

TABLE 3 Performance Values of WR for Differentiation of Benign from Malignant Tumors

in Index terms:
by Hidetake Yabuuchi Md, Tatsuro Fukuya Md, Tsuyoshi Tajima Md, Yoichi Hachitanda Md, Kichinobu Tomita Md, Mitsuru Koga Md 2002

Table 4: Fractal Dimension

in An Analysis of Local Feature Extraction in Digital Mammography
by David J. Marchette, David J. Marchette, Richard A. Lorey, Richard A. Lorey, Carey E. Priebe, Carey E. Priebe
"... In PAGE 8: ... A purist might insist that since we fail to reject at the .05 level in Table4 , we cannot state that we have a significant Table 1: Coefficient of Variation Test div p-value Wilcoxon 0.0008 Paired t-Test 0.... ..."

Table 2. Most commonly reported types of calcification BENIGN calcifications

in CADMIUM II: Acquisition and Representation of Radiological Knowledge for Computerized Decision Support in Mammography
by Eugenio Alberdi, Paul Taylor, Richard Lee, John Fox, Margarita Sordo, Andrew Todd-Pokropek
"... In PAGE 3: ... Part of the analysis focused on the 45 calcifications which were reported by more than half of the radiologists (about 50% of the 91 calcifications reported in the study). These are shown in Table2 . The groupings indicate whether the calcifications were diagnosed by most of the radiologists who reported them as benign, malignant or indeterminate; the group discrepant diagnoses includes the calcifications for which there was no diagnosis on which the majority of the participants agreed.... In PAGE 3: ... The groupings indicate whether the calcifications were diagnosed by most of the radiologists who reported them as benign, malignant or indeterminate; the group discrepant diagnoses includes the calcifications for which there was no diagnosis on which the majority of the participants agreed. Further, the benign calcifications could be classified in 10 distinct groups, as a function of the descriptors most commonly associated with them and, similarly, the malignant ones were classified into two distinct groups (see Table2 ). The first five groups of calcifications in the benign group were diagnosed as benign by all the radiologists who reported them.... In PAGE 3: ... For the rest of the benign calcifications and for the malignant and indeterminate ones there was no unanimity but agreement was always over 75%. The descriptors presented in Table2 are those which were used for all the calcifications in each group by two or more of the radiologists. Most of the calcifications in the table (especially those unanimously diagnosed as benign) were described with at least one property value which belonged to the set of discriminating descriptors shown in Table 1.... In PAGE 4: ...g. smudgy and punctate Table2 ). These combinations are represented as arguments supporting (though not confirming ) either candidate.... ..."

Table 4: Confusion matrix with recognition scores classi#0Ccation benign-malignant of #2838#29 pa-

in Wavelets as Chromatin Texture Descriptors for the Automated Identification of Neoplastic Nuclei
by G. Van De Wouwer, B. Weyn, P. Scheunders, W. Jacob, E. Van Marck, D. Van Dyck
"... In PAGE 8: ...2.1 Diagnostic classi#0Ccation - benign#2Fmalignant The confusion matrix for the classi#0Ccation of a patient as either benign or malignant is presented in Table4 . A CCR of 100.... ..."
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