AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification
Abstract
:1. Introduction
- Proposal of a novel FS method based on improving the behavior of the Artificial Hummingbird Algorithm using Aquila Optimization. This model aims to choose the most important features from each image representation to make the classification process more efficient (using a reduced set of features).
- Presentation of a comprehensive experimental study of the proposed system with a comparison of the proposed method with various state-of-the-art methods by utilizing four real-world datasets.
2. Related Work
3. Background
3.1. Efficient Neural Networks
3.2. Aquila Optimizer (AO)
3.3. Artificial Hummingbird Algorithm
3.3.1. Guided Foraging
3.3.2. Territorial Foraging
3.3.3. Migration Foraging
4. Proposed Method
4.1. Deep Learning for Feature Extraction
4.2. Feature-Selection-Based AHA-AO
5. Experimental Results
5.1. Performance Measures
5.2. Experiment 1: Results without the Feature Selection Optimization
5.3. Experiment 2: Results Based on the ISIC-2016 Dataset
5.4. Experiment 3: Results Based on the PH2 Dataset
5.5. Experiment 4: Results Based on the Chest-XRay Dataset
5.6. Experiment 5: Results Based on the Blood-Cell Dataset
5.7. Comparison with Studies in the Literature
6. Limitations of the Study and Future Work
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Algorithm | Parameter Settings |
---|---|
PSO | VMax = 6, WMax = 0.9, WMin = 0.2 |
MFO | |
WOA | = −1 to −2 |
AO | , , |
AHA | |
AHA-AO | , , , |
Dataset Name | Class | Training Data | Test Data | Total Images |
---|---|---|---|---|
ISIC-2016 | Malignant | 173 | 75 | 248 |
Benign | 727 | 304 | 1031 | |
Common Nevus | 68 | 12 | 80 | |
Atypical Nevus | 68 | 12 | 80 | |
Melanoma | 34 | 6 | 40 | |
Chest-XRay | Normal | 1349 | 234 | 1583 |
Pneumonia | 3883 | 390 | 4273 | |
Blood-Cell | Neutrophil | 2499 | 624 | 3123 |
Monocyte | 2478 | 620 | 3098 | |
Lymphocyte | 2483 | 620 | 3103 | |
Eosinophil | 2497 | 623 | 3120 |
Dataset | Classifier | Accuracy | Recall | Precision | F1-Score |
---|---|---|---|---|---|
ISIC-2016 | DT | 0.8259 | 0.8258 | 0.8192 | 0.8221 |
LDA | 0.8602 | 0.8601 | 0.8518 | 0.8541 | |
SVM | 0.8602 | 0.8601 | 0.8546 | 0.8567 | |
PH2 | DT | 0.9179 | 0.9178 | 0.9206 | 0.9177 |
LDA | 0.9536 | 0.9535 | 0.955 | 0.9535 | |
SVM | 0.9571 | 0.9571 | 0.9574 | 0.9572 | |
Chest-XRay | DT | 0.8253 | 0.8253 | 0.8384 | 0.8156 |
LDA | 0.8478 | 0.8477 | 0.8739 | 0.8373 | |
SVM | 0.8718 | 0.8717 | 0.8906 | 0.8651 | |
Blood-Cell | DT | 0.8786 | 0.8785 | 0.9001 | 0.8809 |
LDA | 0.8834 | 0.8833 | 0.9041 | 0.8853 | |
SVM | 0.8846 | 0.8845 | 0.905 | 0.8865 |
Alg. | Model | Accuracy | BA | F1-Score | Recall | Precision | Time | No. of Features |
---|---|---|---|---|---|---|---|---|
PSO | DT | 0.8153 | 0.6991 | 0.8134 | 0.8153 | 0.8117 | 0.0449 | 86 |
LDA | 0.8549 | 0.7438 | 0.8504 | 0.8549 | 0.8479 | 0.0463 | ||
SVM | 0.8628 | 0.7488 | 0.8573 | 0.8628 | 0.8551 | 0.155 | ||
MFO | DT | 0.7995 | 0.6641 | 0.7951 | 0.7995 | 0.7916 | 0.044 | 58 |
LDA | 0.8575 | 0.7455 | 0.8527 | 0.8575 | 0.8503 | 0.0511 | ||
SVM | 0.8628 | 0.7437 | 0.8564 | 0.8628 | 0.8544 | 0.1232 | ||
WOA | DT | 0.8074 | 0.6791 | 0.8038 | 0.8074 | 0.8008 | 0.0395 | 56 |
LDA | 0.8602 | 0.7571 | 0.8567 | 0.8602 | 0.8546 | 0.0379 | ||
SVM | 0.8549 | 0.7438 | 0.8504 | 0.8549 | 0.8479 | 0.148 | ||
AO | DT | 0.7942 | 0.6859 | 0.7962 | 0.7942 | 0.7984 | 0.039 | 53 |
LDA | 0.8628 | 0.7387 | 0.8554 | 0.8628 | 0.8538 | 0.0373 | ||
SVM | 0.8575 | 0.7304 | 0.8499 | 0.8575 | 0.8478 | 0.1196 | ||
AHA | DT | 0.8285 | 0.7274 | 0.8281 | 0.8285 | 0.8276 | 0.0492 | 60 |
LDA | 0.8681 | 0.747 | 0.861 | 0.8681 | 0.8598 | 0.038 | ||
SVM | 0.8628 | 0.7337 | 0.8544 | 0.8628 | 0.8533 | 0.1225 | ||
AHA-AO | DT | 0.8179 | 0.7208 | 0.8193 | 0.8179 | 0.8207 | 0.0398 | 52 |
LDA | 0.8628 | 0.7588 | 0.859 | 0.8628 | 0.8569 | 0.0349 | ||
SVM | 0.8734 | 0.7654 | 0.8683 | 0.8734 | 0.8667 | 0.1132 |
Alg. | Model | Accuracy | BA | F1-Score | Recall | Precision | Time | No. of Features |
---|---|---|---|---|---|---|---|---|
PSO | DT | 0.8893 | 0.9048 | 0.89 | 0.8893 | 0.9037 | 0.1484 | 326 |
LDA | 0.9643 | 0.9702 | 0.9643 | 0.9643 | 0.9648 | 0.2309 | ||
SVM | 0.9643 | 0.9702 | 0.9643 | 0.9643 | 0.9644 | 0.1442 | ||
MFO | DT | 0.9179 | 0.9167 | 0.9185 | 0.9179 | 0.9245 | 0.0813 | 222 |
LDA | 0.9607 | 0.9673 | 0.9607 | 0.9607 | 0.9614 | 0.1393 | ||
SVM | 0.9643 | 0.9702 | 0.9643 | 0.9643 | 0.9644 | 0.0947 | ||
WOA | DT | 0.8786 | 0.869 | 0.8794 | 0.8786 | 0.8991 | 0.0927 | 221 |
LDA | 0.9679 | 0.9732 | 0.9679 | 0.9679 | 0.9681 | 0.1445 | ||
SVM | 0.9679 | 0.9732 | 0.9679 | 0.9679 | 0.9679 | 0.0961 | ||
AO | DT | 0.9179 | 0.9226 | 0.9179 | 0.9179 | 0.9261 | 0.0708 | 159 |
LDA | 0.9607 | 0.9673 | 0.9607 | 0.9607 | 0.961 | 0.1135 | ||
SVM | 0.9643 | 0.9702 | 0.9643 | 0.9643 | 0.9643 | 0.0764 | ||
AHA | DT | 0.925 | 0.9256 | 0.9251 | 0.925 | 0.9293 | 0.0602 | 141 |
LDA | 0.9643 | 0.9702 | 0.9643 | 0.9643 | 0.9643 | 0.1095 | ||
SVM | 0.9607 | 0.9673 | 0.9607 | 0.9607 | 0.961 | 0.0773 | ||
AHA-AO | DT | 0.9107 | 0.9048 | 0.9115 | 0.9107 | 0.9192 | 0.0482 | 107 |
LDA | 0.9571 | 0.9643 | 0.9571 | 0.9571 | 0.9573 | 0.0937 | ||
SVM | 0.975 | 0.9792 | 0.975 | 0.975 | 0.975 | 0.0523 |
Alg. | Model | Accuracy | BA | F1-Score | Recall | Precision | Time | No. of Features |
---|---|---|---|---|---|---|---|---|
PSO | DT | 0.8013 | 0.7487 | 0.7875 | 0.8013 | 0.8177 | 0.3909 | 79 |
LDA | 0.8446 | 0.7953 | 0.8339 | 0.8446 | 0.87 | 0.1306 | ||
SVM | 0.8478 | 0.8004 | 0.838 | 0.8478 | 0.8706 | 0.7182 | ||
MFO | DT | 0.8141 | 0.7641 | 0.802 | 0.8141 | 0.8307 | 0.4391 | 91 |
LDA | 0.8462 | 0.7983 | 0.8361 | 0.8462 | 0.8694 | 0.1405 | ||
SVM | 0.8574 | 0.8132 | 0.8492 | 0.8574 | 0.8774 | 0.7427 | ||
WOA | DT | 0.8237 | 0.7778 | 0.8137 | 0.8237 | 0.8371 | 0.4166 | 98 |
LDA | 0.8397 | 0.788 | 0.8278 | 0.8397 | 0.8685 | 0.1709 | ||
SVM | 0.8558 | 0.8103 | 0.847 | 0.8558 | 0.8778 | 0.6557 | ||
AO | DT | 0.8189 | 0.7722 | 0.8085 | 0.8189 | 0.8321 | 0.419 | 91 |
LDA | 0.8446 | 0.7944 | 0.8336 | 0.8446 | 0.8718 | 0.1464 | ||
SVM | 0.8558 | 0.8111 | 0.8473 | 0.8558 | 0.8763 | 0.6305 | ||
AHA | DT | 0.8061 | 0.756 | 0.7937 | 0.8061 | 0.8204 | 0.5743 | 99 |
LDA | 0.851 | 0.8038 | 0.8414 | 0.851 | 0.8744 | 0.1867 | ||
SVM | 0.8542 | 0.8081 | 0.8451 | 0.8542 | 0.8767 | 0.6737 | ||
AHA-AO | DT | 0.8269 | 0.7812 | 0.8171 | 0.8269 | 0.8409 | 0.5981 | 96 |
LDA | 0.8494 | 0.8017 | 0.8396 | 0.8494 | 0.8733 | 0.1783 | ||
SVM | 0.8686 | 0.8274 | 0.8617 | 0.8686 | 0.8869 | 0.6623 |
Alg. | Model | Accuracy | BA | F1-Score | Recall | Precision | Time | No. of Features |
---|---|---|---|---|---|---|---|---|
PSO | DT | 0.8733 | 0.8733 | 0.8764 | 0.8733 | 0.8978 | 2.0821 | 347 |
LDA | 0.8838 | 0.8837 | 0.8859 | 0.8838 | 0.9053 | 1.2637 | ||
SVM | 0.8858 | 0.8858 | 0.8877 | 0.8858 | 0.906 | 0.8885 | ||
MFO | DT | 0.881 | 0.8809 | 0.8824 | 0.881 | 0.8976 | 1.5234 | 225 |
LDA | 0.8814 | 0.8813 | 0.8834 | 0.8814 | 0.9031 | 0.738 | ||
SVM | 0.8838 | 0.8838 | 0.8859 | 0.8838 | 0.9055 | 0.7944 | ||
WOA | DT | 0.8778 | 0.8777 | 0.88 | 0.8778 | 0.9005 | 1.4109 | 226 |
LDA | 0.8806 | 0.8805 | 0.8828 | 0.8806 | 0.9027 | 0.7313 | ||
SVM | 0.8838 | 0.8838 | 0.8856 | 0.8838 | 0.9041 | 0.7249 | ||
AO | DT | 0.8806 | 0.8805 | 0.8822 | 0.8806 | 0.898 | 0.8553 | 125 |
LDA | 0.879 | 0.8789 | 0.8811 | 0.879 | 0.9014 | 0.5041 | ||
SVM | 0.8838 | 0.8838 | 0.886 | 0.8838 | 0.9058 | 0.6661 | ||
AHA | DT | 0.8721 | 0.8721 | 0.8753 | 0.8721 | 0.8977 | 0.8567 | 132 |
LDA | 0.8818 | 0.8817 | 0.884 | 0.8818 | 0.9037 | 0.4571 | ||
SVM | 0.8846 | 0.8846 | 0.8863 | 0.8846 | 0.9035 | 0.53 | ||
AHA-AO | DT | 0.8749 | 0.8749 | 0.877 | 0.8749 | 0.8956 | 0.4053 | 65 |
LDA | 0.8826 | 0.8825 | 0.8844 | 0.8826 | 0.903 | 0.1887 | ||
SVM | 0.8862 | 0.8862 | 0.8878 | 0.8862 | 0.9053 | 0.2579 |
Dataset | Model | Accuracy (%) | Year | Ref. |
---|---|---|---|---|
ISIC-2016 | CUMED | 85.50 | 2016 | [34] |
BL-CNN | 85.00 | 2017 | [32] | |
DCNN-FV | 86.81 | 2018 | [39] | |
MC-CNN | 86.30 | 2019 | [40] | |
MFA | 86.81 | 2020 | [41] | |
AHA-AO | 87.30 | present | Our | |
PH2 | ANN | 92.50 | 2017 | [60] |
Kernel Sparse | 93.50 | 2019 | [61] | |
DenseNet201 + SVM | 92.00 | 2020 | [62] | |
DenseNet201 + KNN | 93.16 | 2020 | [63] | |
ResNet50 + NB | 95.40 | 2021 | [64] | |
AHA-AO | 97.50 | present | Our | |
Chest-XRay | DCGAN | 84.19 | 2018 | [65] |
DenseNet121 | 86.80 | 2021 | [66] | |
AHA-AO | 86.90 | present | Our | |
Blood-Cell | CNN + SVM | 85.00 | 2013 | [67] |
CNN | 87.08 | 2017 | [68] | |
CNN + Augmentation | 87.00 | 2019 | [69] | |
AHA-AO | 88.60 | present | Our |
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Elaziz, M.A.; Dahou, A.; El-Sappagh, S.; Mabrouk, A.; Gaber, M.M. AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification. Appl. Sci. 2022, 12, 9710. https://doi.org/10.3390/app12199710
Elaziz MA, Dahou A, El-Sappagh S, Mabrouk A, Gaber MM. AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification. Applied Sciences. 2022; 12(19):9710. https://doi.org/10.3390/app12199710
Chicago/Turabian StyleElaziz, Mohamed Abd, Abdelghani Dahou, Shaker El-Sappagh, Alhassan Mabrouk, and Mohamed Medhat Gaber. 2022. "AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification" Applied Sciences 12, no. 19: 9710. https://doi.org/10.3390/app12199710
APA StyleElaziz, M. A., Dahou, A., El-Sappagh, S., Mabrouk, A., & Gaber, M. M. (2022). AHA-AO: Artificial Hummingbird Algorithm with Aquila Optimization for Efficient Feature Selection in Medical Image Classification. Applied Sciences, 12(19), 9710. https://doi.org/10.3390/app12199710