2015
DOI: 10.1007/978-3-319-16483-0_20
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Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier

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Cited by 84 publications
(58 citation statements)
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References 27 publications
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“…The results are shown in Table 4 and Figure 2. From Figure 2 we can observe that this proposed "GLCM+ELM" method gives better performance than the two basis methods, NBC [30] and WE [31], in terms of sensitivity, specificity, and accuracy. This demonstrates the effectiveness of GLCM.…”
Section: Experiments and Resultsmentioning
confidence: 96%
See 1 more Smart Citation
“…The results are shown in Table 4 and Figure 2. From Figure 2 we can observe that this proposed "GLCM+ELM" method gives better performance than the two basis methods, NBC [30] and WE [31], in terms of sensitivity, specificity, and accuracy. This demonstrates the effectiveness of GLCM.…”
Section: Experiments and Resultsmentioning
confidence: 96%
“…The average sensitivity, specificity, and accuracy of our method is 72%, 70%, and 71%, respectively. 1 3 2 4 1 60 80 70 2 5 0 2 3 100 40 70 3 3 2 4 1 60 80 70 4 2 3 2 3 40 40 40 5 3 2 5 0 60 100 80 6 5 0 3 2 100 60 80 7 4 1 4 1 80 80 80 8 3 2 5 0 60 100 80 9 4 1 3 2 80 60 70 10 4 1 3 2 80 60 70 Average 72 70 71 Finally, we compared this proposed "GLCM+ELM" method with state-of-the-art approaches, including naï ve Bayes classifier (NBC) [30] and wavelet energy (WE) [31]. The results are shown in Table 4 and Figure 2.…”
Section: Experiments and Resultsmentioning
confidence: 99%
“…A computer assisted diagnosis method is proposed by Zhou, Xingxing, et al based on a Wavelet entropy of the feature space approach and a Nave Bayes classification method is used for improving the brain diagnosis accuracy by means of Nuclear magnetic resonance images [24]. CNNs are used to train large scale labelled training data.…”
Section: B Headpose Classification Methodsmentioning
confidence: 99%
“…Then, they used a generalized eigenvalue proximal SVM (GEPSVM). Zhou et al [19] used waveletentropy as the feature space, then they employed a Naive Bayes classifier (NBC) classification method. Their results over 64 images showed that the sensitivity of the classifier is 94.50%, the specificity 91.70%, the overall accuracy 92.60%.…”
Section: Existing Pathological Brain Detection Systemsmentioning
confidence: 99%
“…We compared the proposed HBP with BP [37], MBP [38], GA [39], SA [40], 2.4.1 [41], BBO [42], PSO [43], and BPSO [50] III. We compared the proposed HBP-FNN with fourteen state-of-the-art classification methods as DWT + PCA + FP-ANN [7], DWT + PCA + KNN [7], DWT + PCA + SCABC-FNN [8], DWT + PCA + SVM + HPOL [11], DWT + PCA + SVM + IPOL [11], DWT + PCA + SVM + GRB [11], WE + SWP + PNN [12], RT + PCA + LS-SVM [14], PCNN + DWT + PCA + BPNN [17], DWPT + SE + GEPSVM [18], DWPT + TE + GEPSVM [18], WE + NBC [19], WEnergy + SVM [22], and SWT + PCA + HPA-FNN [26]. IV.…”
Section: Experiments Designmentioning
confidence: 99%