DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features
Abstract
:1. Introduction
2. Literature Review
3. Materials and Method
3.1. Dataset
3.2. Dataset Pre-Processing
3.3. Feature Extraction
3.4. Classification
4. Results and Discussion
Receiver Operating Characteristics (ROC) Curve
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8910 | 0.8933 | 0.8922 | 0.8924 | 3528.79 |
SVM | 0.9426 | 0.9557 | 0.9491 | 0.9488 | 71,163.34 |
NB | 0.6283 | 0.9647 | 0.7610 | 0.6970 | 23.01 |
KNN | 0.8866 | 0.9649 | 0.9241 | 0.9207 | 9626.79 |
Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8606 | 0.8582 | 0.8594 | 0.8594 | 2375.60 |
SVM | 0.9384 | 0.9522 | 0.9452 | 0.9448 | 80,246.96 |
NB | 0.6026 | 0.9664 | 0.7423 | 0.6646 | 23.64 |
KNN | 0.8571 | 0.9515 | 0.9019 | 0.8964 | 9635.20 |
Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8689 | 0.8717 | 0.8703 | 0.8705 | 2723.95 |
SVM | 0.9422 | 0.9553 | 0.9487 | 0.9484 | 34,231.91 |
NB | 0.8082 | 0.8767 | 0.8411 | 0.8343 | 11.90 |
KNN | 0.8469 | 0.9476 | 0.8944 | 0.8881 | 5029.97 |
Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8893 | 0.8840 | 0.8845 | 0.8848 | 3315.88 |
SVM | 0.9411 | 0.9560 | 0.9485 | 0.9481 | 30,682.47 |
NB | 0.8295 | 0.8867 | 0.8572 | 0.8522 | 11.86 |
KNN | 0.8311 | 0.9602 | 0.8910 | 0.8825 | 4988.34 |
Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8745 | 0.8682 | 0.8713 | 0.8708 | 1607.55 |
SVM | 0.9255 | 0.9615 | 0.9432 | 0.9421 | 26,035.99 |
NB | 0.7167 | 0.7379 | 0.7271 | 0.7231 | 6.91 |
KNN | 0.7977 | 0.8927 | 0.8426 | 0.8300 | 2671.86 |
Classification Algorithms | Performance Evaluation Metrics | ||||
---|---|---|---|---|---|
Precision | Recall | F1-Score | Accuracy | Time (s) | |
DT | 0.8660 | 0.8614 | 0.8637 | 0.8633 | 3612.66 |
SVM | 0.9293 | 0.9599 | 0.9443 | 0.9434 | 50,593.37 |
NB | 0.6805 | 0.8396 | 0.7517 | 0.7227 | 12.96 |
KNN | 0.7795 | 0.9393 | 0.8520 | 0.8368 | 4246.80 |
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Abubakar, A.; Ajuji, M.; Yahya, I.U. DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features. Appl. Syst. Innov. 2021, 4, 82. https://doi.org/10.3390/asi4040082
Abubakar A, Ajuji M, Yahya IU. DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features. Applied System Innovation. 2021; 4(4):82. https://doi.org/10.3390/asi4040082
Chicago/Turabian StyleAbubakar, Aliyu, Mohammed Ajuji, and Ibrahim Usman Yahya. 2021. "DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features" Applied System Innovation 4, no. 4: 82. https://doi.org/10.3390/asi4040082
APA StyleAbubakar, A., Ajuji, M., & Yahya, I. U. (2021). DeepFMD: Computational Analysis for Malaria Detection in Blood-Smear Images Using Deep-Learning Features. Applied System Innovation, 4(4), 82. https://doi.org/10.3390/asi4040082