Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Acquisition
2.2. Overview of the Proposed Method
2.2.1. Volume Extraction of Liver
2.2.2. Feature Extraction
Texture Features of Four Groups: GLCM, GLGCM, GLCCM, and Tamura
Texture Features of Three Groups: NGTDM, NGTGDM, and NGTCDM
2.2.3. Feature Selection
2.2.4. Hybrid WOA-SA
Hybrid WOA-SA for Optimal Feature Selection
2.2.5. Class Prediction Based on Ensemble Classifier with WMV: SVM, k-NN, RF
Ensemble Approach
2.2.6. Tumor Burden
2.3. Performance Analysis
3. Results and Discussions
3.1. Performance Comparison of Selected Feature Sets in Different Classification Methodologies
3.2. Classification Error Percentage
3.3. Comparison of Classification Performance
3.4. Clinical Feasibility
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Feature Groups | Extracted Features | Description |
---|---|---|
GLCM | Angular second moment | |
Contrast | ||
Correlation | ||
Inverse Different Moment | ||
Homogeneity | ||
Sum Average | ||
Sum Variance | ||
Sum entropy | ||
Entropy | ||
Difference variance | ||
Difference Entropy | ||
The information measure I of correlation | ||
Information measure II of correlation | ||
The range of the Features | ||
GLGCM | 13 Haralick features of GLGCM | |
GLCCM | 13 Haralick features of GLCCM | |
Tamura | Coarseness | |
Contrast | ||
Directionality | ||
Line-likeness | ||
Regularity | ||
Roughness |
Feature Group | Extracted Features | Description |
---|---|---|
NGTDM | Coarseness | |
Contrast | ||
Busyness | ||
Complexity | ||
Texture strength | ||
NGTGDM | Coarseness | |
Contrast | ||
Busyness | ||
Complexity | ||
Texture strength | ||
NGTCDM | Coarseness | |
Contrast | ||
Busyness | ||
Complexity | ||
Texture strength |
Feature Groups | Range of Features | Description |
---|---|---|
GLCM | 1 to 13 | Angular second moment, Contrast, Correlation, Inverse Different Moment, Homogeneity, Sum Average, Sum Variance, Sum entropy, Entropy, Difference variance, Difference Entropy, Information measure of correlation 1, Information measure of correlation 2. |
14 to 26 | Range of corresponding GLCM features from 1 to 13 (listed above) | |
GLGCM | 27 to 39 | Autocorrelation, Contrast, Energy, Entropy, Homogeneity, Maximum-probability, sum-average, Sum-variance, Sum-entropy, Difference-variance, Difference-entropy, Information measure of correlation 1, Information measure of correlation 2 |
GLCCM | 40 to 52 | Autocorrelation, Contrast, Energy, Entropy, Homogeneity, Maximum-probability, sum-average, Sum-variance, Sum-entropy, Difference-variance, Difference-entropy, Information measure of correlation 1, Information measure of correlation 2 |
NGTDM | 53 to 57 | Coarseness, contrast, busyness, complexity, strength |
NGTCDM | 58 to 62 | Coarseness, contrast, busyness, complexity, strength |
NGTGDM | 63 to 67 | Coarseness, contrast, busyness, complexity, strength |
Tamura | 68 to 73 | Coarseness, Contrast, Directionality, Line likeness, Regularity, Roughness |
Sl. No | Feature Combinations | |||
---|---|---|---|---|
Proposed | Rand_Comb1 | Rand_Comb2 | Rand_Comb3 | |
1 | 47 | 37 | 38 | 5 |
2 | 31 | 63 | 29 | 68 |
3 | 6 | 51 | 24 | 16 |
4 | 59 | 67 | 33 | 6 |
5 | 24 | 6 | 67 | 72 |
6 | 22 | 41 | 45 | 36 |
7 | 63 | 55 | 4 | 13 |
8 | 57 | 5 | 63 | 4 |
9 | 44 | 20 | 72 | 53 |
10 | 19 | 15 | 56 | 26 |
11 | 3 | 28 | 48 | 58 |
12 | 51 | 8 | 27 | 32 |
13 | 29 | 13 | 73 | 52 |
14 | 16 | 34 | 30 | 14 |
15 | 32 | 45 | 41 | 69 |
16 | 10 | 12 | 70 | 30 |
17 | 5 | 3 | 15 | 25 |
18 | 12 | 58 | 65 | 49 |
19 | 38 | 64 | 55 | 23 |
20 | 53 | 46 | 6 | 60 |
21 | 30 | 25 | 25 | 8 |
22 | 42 | 21 | 9 | 15 |
23 | 25 | 61 | 35 | 66 |
24 | 41 | 2 | 57 | 45 |
25 | 2 | 31 | 7 | 11 |
Feature Combinations | 95% Confidence Interval—Performance Metrics | |||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Accuracy | Sensitivity | Specificity | ||||||||||
SVM | k-NN | RF | Proposed | SVM | k-NN | RF | Proposed | SVM | k-NN | RF | Proposed | |
1 | (0.50, 0.90) | (0.66, 0.99) | (0.66, 0.99) | (0.78, 1.00) | (0.21, 0.63) | (0.41, 0.83) | (0.41, 0.83) | (0.68, 1.00) | (0.61, 0.97) | (0.70, 1.00) | (0.69, 1.00) | (0.79, 1.00) |
2 | (0.50, 0.90) | (0.70, 1.00) | (0.64, 0.98) | (0.75, 1.00) | (0.07, 0.42) | (0.53, 0.92) | (0.39, 0.82) | (0.60, 0.96) | (0.59, 0.96) | (0.73, 1.00) | (0.69, 1.00) | (0.76, 1.00) |
3 | (0.50, 0.90) | (0.68, 1.00) | (0.70, 1.00) | (0.75, 1.00) | (0.10, 0.48) | (0.43, 0.85) | (0.53, 0.92) | (0.60, 0.96) | (0.61, 0.97) | (0.71, 1.00) | (0.72, 1.00) | (0.76, 1.00) |
4 | (0.50, 0.90) | (0.68, 1.00) | (0.66, 0.99) | (0.78, 1.00) | (0.17, 0.59) | (0.43, 0.85) | (0.41, 0.83) | (0.68, 1.00) | (0.61, 0.97) | (0.72, 1.00) | (0.69, 1.00) | (0.79, 1.00) |
5 | (0.42, 0.84) | (0.68, 1.00) | (0.70, 1.00) | (0.75, 1.00) | (0.05, 0.39) | (0.43, 0.85) | (0.45, 0.86) | (0.60, 0.96) | (0.54, 0.93) | (0.71, 1.00) | (0.72, 1.00) | (0.76, 1.00) |
6 | (0.46, 0.87) | (0.70, 1.00) | (0.70, 1.00) | (0.75, 1.00) | (0.07, 0.43) | (0.45, 0.86) | (0.45, 0.86) | (0.60, 0.96) | (0.58, 0.95) | (0.71, 1.00) | (0.74, 1.00) | (0.77, 1.00) |
7 | (0.59, 0.96) | (0.68, 1.00) | (0.66, 0.99) | (0.75, 1.00) | (0.36, 0.79) | (0.51, 0.91) | (0.41, 0.83) | (0.60, 0.96) | (0.67, 1.00) | (0.70, 1.00) | (0.69, 1.00) | (0.76, 1.00) |
8 | (0.46, 0.87) | (0.66, 0.99) | (0.68, 1.00) | (0.75, 1.00) | (0.07, 0.43) | (0.41, 0.83) | (0.43, 0.85) | (0.60, 0.96) | (0.57, 0.94) | (0.69, 1.00) | (0.70, 1.00) | (0.76, 1.00) |
Liver Diseases | Tumor Burden Rate (%) |
---|---|
Fatty | 14.87 |
Metastasis | 11.23 |
Cancer | 29.43 |
Cirrhosis | 17.05 |
Methods | Error Rate (%) |
---|---|
SVM | 17.14 ± 0.1321 |
k-NN | 7.62 ± 0.0621 |
RF | 11.43 ± 0.0522 |
Proposed Method | 1.90 ± 0.0522 |
Test Class Labels | Performance Metrics | Comparative Methods | |||||
---|---|---|---|---|---|---|---|
Existing Method [43] | Existing Method [69] | Proposed Method | |||||
PNN | LVQ | BPN | PNN | SVM | Ensemble | ||
Fatty | Accuracy | 0.76 (0.5226, 0.9170) | 0.80 (0.5641, 0.9431) | 0.80 (0.5641, 0.9431) | 0.61 (0.3787, 0.8073) | 0.85 (0.6184, 0.9733) | 0.90 (0.6760, 1.0000) |
Sensitivity | 0.79 (0.5536, 0.9367) | 0.61 (0.3787, 0.8073) | 0.65 (0.4151, 0.8382) | 0.81 (0.5747, 0.9494) | 0.76 (0.5226, 0.9170) | 1.00 (0.8076, 1.0000) | |
Specificity | 0.84 (0.6073, 0.9675) | 0.81 (0.5747, 0.9494) | 0.81 (0.5747, 0.9494) | 0.81 (0.5747, 0.9494) | 0.94 (0.7253, 1.0000) | 0.88 (0.6525, 0.9899) | |
Metastasis | Accuracy | 0.95 (0.7382, 1.0000) | 0.14 (0.0362, 0.3551) | 0.85 (0.6184, 0.9733) | 0.95 (0.7382, 1.0000) | 0.95 (0.7382, 1.0000) | 0.95 (0.7382, 1.0000) |
Sensitivity | 0.80 (0.5641, 0.9431) | 0.63 (0.3969, 0.8229) | 0.67 (0.4342, 0.8532) | 0.81 (0.5747, 0.9494) | 0.81 (0.5747, 0.9494) | 0.66 (0.4248, 0.8457) | |
Specificity | 0.78 (0.5431, 0.9303) | 0.81 (0.5747, 0.9494) | 0.81 (0.5747, 0.9494) | 0.81 (0.5747, 0.9494) | 0.91 (0.6880, 1.0000) | 1.00 (0.8076, 1.0000) | |
Cancer | Accuracy | 0.95 (0.7382, 1.0000) | 0.90 (0.6760, 1.0000) | 0.90 (0.6760, 1.0000) | 0.95 (0.7382, 1.0000) | 0.95 (0.7382, 1.0000) | 0.95 (0.7382, 1.0000) |
Sensitivity | 0.72 (0.4825, 0.8895) | 0.64 (0.4061, 0.8306) | 0.68 (0.4437, 0.8606) | 0.83 (0.5963, 0.9616) | 0.78 (0.5431, 0.9303) | 0.50 (0.2834, 0.7166) | |
Specificity | 0.89 (0.6642, 0.9952) | 0.81 (0.5747, 0.9494) | 0.83 (0.5963, 0.9616) | 0.83 (0.5963, 0.9616) | 0.88 (0.6525, 0.9899) | 1.00 (0.8076, 1.0000) | |
Cirrhosis | Accuracy | 1.00 (0.8076, 1.0000) | 0.90 (0.6760, 1.0000) | 0.90 (0.6760, 1.0000) | 0.95 (0.7382, 1.0000) | 0.95 (0.7382, 1.0000) | 1.00 (0.8076, 1.0000) |
Sensitivity | 0.78 (0.5431, 0.9303) | 0.61 (0.3787, 0.8073) | 0.66 (0.4248, 0.8457) | 0.85 (0.6184, 0.9733) | 0.75 (0.5124, 0.9103) | 1.00 (0.8076, 1.0000) | |
Specificity | 0.91 (0.6880, 1.0000) | 0.79 (0.5536, 0.9367) | 0.85 (0.6184, 0.9733) | 0.85 (0.6184, 0.9733) | 0.90 (0.6760, 1.0000) | 1.00 (0.8076, 1.0000) | |
Normal | Accuracy | 0.85 (0.6184, 0.9733) | 0.52 (0.3002, 0.7337) | 0.47 (0.2589, 0.6904) | 0.76 (0.5226, 0.9170) | 0.90 (0.6760, 1.0000) | 1.00 (0.8076, 1.0000) |
Sensitivity | 0.75 (0.5124, 0.9103) | 0.64 (0.4061, 0.8306) | 0.67 (0.4342, 0.8532) | 0.85 (0.6184, 0.9733) | 0.82 (0.5855, 0.9555) | 1.00 (0.8076, 1.0000) | |
Specificity | 0.85 (0.6184, 0.9733) | 0.79 (0.5536, 0.9367) | 0.85 (0.6184, 0.9733) | 0.85 (0.6184, 0.9733) | 0.92 (0.7002, 1.000) | 1.00 (0.8076, 1.0000) |
Parameter | Solution Methods | |||||
---|---|---|---|---|---|---|
Existing Method [43] | Existing Method [69] | Proposed Method | ||||
PNN | LVQ | BPN | PNN | SVM | ||
Accuracy | 0.90 (0.6760, 1.0000) | 0.65 (0.4154, 0.8382) | 0.79 (0.4154, 0.8382) | 0.84 (0.6073, 0.9675) | 0.92 (0.7002, 1.000) | 0.98 (0.7786, 1.0000) |
Sensitivity | 0.77 (0.5328, 0.9237) | 0.65 (0.4154, 0.8382) | 0.69 (0.4154, 0.8382) | 0.77 (0.5328, 0.9237) | 0.87 (0.6410, 0.9845) | 0.96 (0.7513, 1.0000) |
Specificity | 0.88 (0.6525, 0.9899) | 0.82 (0.5855, 0.9555) | 0.85 (0.6184, 0.9733) | 0.83 (0.5963, 0.9616) | 0.91 (0.6880, 1.0000) | 0.93 (0.7126, 1.0000) |
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Share and Cite
Rajathi, G.I.; Jiji, G.W. Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier. Symmetry 2019, 11, 33. https://doi.org/10.3390/sym11010033
Rajathi GI, Jiji GW. Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier. Symmetry. 2019; 11(1):33. https://doi.org/10.3390/sym11010033
Chicago/Turabian StyleRajathi, G. Ignisha, and G. Wiselin Jiji. 2019. "Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier" Symmetry 11, no. 1: 33. https://doi.org/10.3390/sym11010033
APA StyleRajathi, G. I., & Jiji, G. W. (2019). Chronic Liver Disease Classification Using Hybrid Whale Optimization with Simulated Annealing and Ensemble Classifier. Symmetry, 11(1), 33. https://doi.org/10.3390/sym11010033