Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features
Abstract
:1. Introduction
- Improving blood slide images using overlapping average filters and Contrast limited adaptive histogram equalization (CLAHE);
- Classification of WBC types by SVM based on hybrid features of VGG19-ResNet101, ResNet101-MobileNet and VGG19-ResNet101-MobileNet;
- Classification of WBC types by FFNN based on hybrid features of CNN (VGG19, ResNet101 and MobileNet) and handcrafted features.
2. Related Work
3. Materials and Methods
3.1. Description of the WBC Type Dataset
3.2. Enhancement Images of Blood Smears for WBC Type
3.3. CNN-SVM Technique
3.3.1. CNN Models for Feature Extraction
3.3.2. SVM Algorithm
3.4. FFNN with Fused CNN and Handcrafted Features
4. Results of Techniques Performance
4.1. Split of WBC Dataset
4.2. System Performance Metrics
4.3. Results of CNN-SVM Technique
4.4. Results of FFNN with Fused Features of CNN and Handcrafted
4.4.1. Error Histogram
4.4.2. Cross-Entropy
4.4.3. Gradient and Validation Checks
5. Discussion of the Systems Performance for Classifying WBC Types
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Phase | 80% (80:20) | Testing 20% | |
---|---|---|---|
Classes | Training (80%) | Validation (20%) | |
Eosinophil | 2005 | 501 | 627 |
Lymphocyte | 1989 | 497 | 622 |
Monocyte | 1981 | 495 | 619 |
Neutrophil | 2030 | 507 | 634 |
Techniques | Classes of WBC | AUC % | Accuracy % | Precision % | Specificity % | Sensitivity % |
---|---|---|---|---|---|---|
VGG19-SVM | Eosinophil | 96.4 | 96.2 | 92.9 | 97.6 | 95.8 |
Lymphocyte | 93.9 | 95.7 | 97.5 | 98.7 | 96.1 | |
Monocyte | 94.5 | 95.8 | 97.1 | 99.2 | 95.5 | |
Neutrophil | 96.1 | 97 | 97.3 | 99.4 | 97.2 | |
average ratio | 95.23 | 96.20 | 96.20 | 98.73 | 96.15 | |
ResNet101-SVM | Eosinophil | 97.1 | 96.2 | 92.8 | 97.1 | 96.2 |
Lymphocyte | 96.8 | 95.8 | 97.7 | 98.6 | 96 | |
Monocyte | 98.2 | 96.3 | 96.9 | 99.2 | 95.8 | |
Neutrophil | 95.6 | 96.2 | 97.3 | 98.7 | 96.3 | |
average ratio | 96.93 | 96.10 | 96.18 | 98.40 | 96.08 | |
MobileNet-SVM | Eosinophil | 98.3 | 97.4 | 93.8 | 98.4 | 97.3 |
Lymphocyte | 97.9 | 96.5 | 99.3 | 99.5 | 95.8 | |
Monocyte | 97.5 | 96.8 | 97.7 | 99.2 | 96.8 | |
Neutrophil | 96.8 | 97.2 | 96.8 | 98.6 | 97.2 | |
average ratio | 97.63 | 97.00 | 96.90 | 98.93 | 96.78 |
Techniques | Classes of WBC | AUC % | Accuracy % | Precision % | Specificity % | Sensitivity % |
---|---|---|---|---|---|---|
VGG19-ResNet101-SVM | Eosinophil | 98.6 | 97.4 | 95.8 | 98.1 | 97.2 |
Lymphocyte | 97.9 | 97.7 | 98.5 | 99.6 | 98.4 | |
Monocyte | 99.1 | 97.7 | 98.5 | 99.8 | 98.1 | |
Neutrophil | 98.8 | 97.3 | 97.5 | 98.8 | 96.7 | |
Average ratio | 98.60 | 97.60 | 97.58 | 99.08 | 97.60 | |
ResNet101-MobileNet-SVM | Eosinophil | 97.9 | 98.4 | 96.9 | 99.1 | 97.7 |
Lymphocyte | 98.5 | 96.8 | 99.5 | 99.6 | 97.3 | |
Monocyte | 99.4 | 98.1 | 98.7 | 99.5 | 98.1 | |
Neutrophil | 99.1 | 99.2 | 97.5 | 98.8 | 99.2 | |
Average ratio | 98.73 | 98.10 | 98.15 | 99.25 | 98.08 | |
VGG19-ResNet101-MobileNet-SVM | Eosinophil | 98.5 | 97.8 | 98.8 | 100 | 98.4 |
Lymphocyte | 98.8 | 98.7 | 99.2 | 99.7 | 99.2 | |
Monocyte | 99.5 | 98.1 | 97.9 | 99.1 | 97.8 | |
Neutrophil | 98.7 | 98.9 | 97.4 | 99.3 | 98.9 | |
Average ratio | 98.88 | 98.40 | 98.33 | 99.53 | 98.58 |
Techniques | Classes of WBC | AUC % | Accuracy % | Precision % | Specificity % | Sensitivity % |
---|---|---|---|---|---|---|
FFNN-handcrafted features | Eosinophil | 92.5 | 89.8 | 90.4 | 96.8 | 90.4 |
Lymphocyte | 93.6 | 98.4 | 98.9 | 99.5 | 98.1 | |
Monocyte | 94.1 | 99 | 97.1 | 98.7 | 99.3 | |
Neutrophil | 92.8 | 90.7 | 91.4 | 97.2 | 90.8 | |
Average ratio | 93.25 | 94.40 | 94.45 | 98.05 | 94.65 |
Techniques | Classes of WBC | AUC % | Accuracy % | Precision % | Specificity % | Sensitivity % |
---|---|---|---|---|---|---|
FFNN with VGG19-handcrafted | Eosinophil | 99.2 | 99.7 | 99.4 | 99.7 | 99.7 |
Lymphocyte | 99.6 | 98.9 | 99.8 | 99.6 | 98.8 | |
Monocyte | 99.4 | 99.8 | 99.2 | 99.5 | 99.6 | |
Neutrophil | 99.4 | 99.4 | 99.4 | 99.8 | 99.2 | |
Average ratio | 99.40 | 99.40 | 99.45 | 99.65 | 99.33 | |
FFNN with ResNet101-handcrafted | Eosinophil | 99.2 | 99.7 | 97.4 | 99.2 | 99.7 |
Lymphocyte | 99.7 | 98.9 | 99.4 | 99.6 | 98.8 | |
Monocyte | 99.4 | 98.4 | 99.5 | 99.7 | 98.5 | |
Neutrophil | 99.6 | 98.7 | 99.5 | 99.5 | 99.2 | |
Average ratio | 99.48 | 98.90 | 98.95 | 99.50 | 99.05 | |
FFNN with MobileNet-handcrafted | Eosinophil | 99.6 | 100 | 99.5 | 99.8 | 100 |
Lymphocyte | 99.5 | 99.2 | 99.8 | 99.5 | 99.5 | |
Monocyte | 99.7 | 99.8 | 100 | 99.7 | 99.7 | |
Neutrophil | 98.9 | 100 | 99.7 | 100 | 99.8 | |
Average ratio | 99.43 | 99.80 | 99.75 | 99.75 | 99.68 |
Techniques | Features | Eosinophil | Lymphocyte | Monocyte | Neutrophil | Accuracy % | |
---|---|---|---|---|---|---|---|
SVM | VGG19-PCA | 96.2 | 95.7 | 95.8 | 97 | 96.2 | |
ResNet101-PCA | 96.2 | 95.8 | 96.3 | 96.2 | 96.1 | ||
MobileNet-PCA | 97.4 | 96.5 | 96.8 | 97.2 | 97 | ||
Fusion features | VGG19-ResNet101 | 97.4 | 97.7 | 97.7 | 97.3 | 97.6 | |
ResNet101-MobileNet | 98.4 | 96.8 | 98.1 | 99.2 | 98.1 | ||
VGG19-ResNet101-MobileNet | 97.8 | 98.7 | 98.1 | 98.9 | 98.4 | ||
FFNN | Handcrafted features | 89.8 | 98.4 | 99 | 90.7 | 94.4 | |
Fusion features | VGG19-hancrafted | 99.7 | 98.9 | 99.8 | 99.4 | 99.4 | |
ResNet-101-handcrafted | 99.7 | 98.9 | 98.4 | 98.7 | 98.9 | ||
MobileNet-handcrafted | 100 | 99.2 | 99.8 | 100 | 99.8 |
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Olayah, F.; Senan, E.M.; Ahmed, I.A.; Awaji, B. Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features. Diagnostics 2023, 13, 1899. https://doi.org/10.3390/diagnostics13111899
Olayah F, Senan EM, Ahmed IA, Awaji B. Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features. Diagnostics. 2023; 13(11):1899. https://doi.org/10.3390/diagnostics13111899
Chicago/Turabian StyleOlayah, Fekry, Ebrahim Mohammed Senan, Ibrahim Abdulrab Ahmed, and Bakri Awaji. 2023. "Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features" Diagnostics 13, no. 11: 1899. https://doi.org/10.3390/diagnostics13111899
APA StyleOlayah, F., Senan, E. M., Ahmed, I. A., & Awaji, B. (2023). Blood Slide Image Analysis to Classify WBC Types for Prediction Haematology Based on a Hybrid Model of CNN and Handcrafted Features. Diagnostics, 13(11), 1899. https://doi.org/10.3390/diagnostics13111899