Malaria Detection Using Advanced Deep Learning Architecture
(This article belongs to the Section Intelligent Sensors)
Abstract
:1. Introduction
2. Epidemiology
3. Life Cycle
4. Clinical Features of Malaria and General Pathogenesis
5. Diagnosis
6. Treatment and Prevention
7. Proposed Solution
Algorithm 1 NAdam training algorithm. |
|
NAdam Algorithm
8. The Dataset
8.1. Labels
- Healthy cells;
- Infected cells.
8.2. Preprocessing
8.3. Normalization
8.4. Augmentation
- Horizontal flip;
- Vertical flip;
- Random noise addition;
- Random rotation;
- Random zoom;
- Random hue shift.
9. Our System
10. Hardware
- CPU: Ryzen Threadripper 2950X 16c/32t;
- RAM: 128GB;
- GPU: NVidia RTX 3090 24GB.
11. Results
Visualization
Algorithm 2 Cells counting algorithm. |
|
12. Conclusions
13. Future Possibilities
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Article | Type | Year | Accuracy |
---|---|---|---|
Ours | Seg-Sem CNN | 2022 | 99.68% |
Divyansh et al. [48] | CNN | 2020 | 95.7% |
Razin et al. [49] | YOLOv5 CNN | 2022 | 96.21% |
Alqudah et al. [50] | Lightweight CNN | 2020 | 98.85% |
Quan et al. [51] | ADCN | 2020 | 97.47% |
Turuk et al. [52] | Integrated CNN | 2022 | 93.89% |
Shekar et al. [53] | Fine-Tuned CNN | 2020 | 95.99% |
Rahman et al. [54] | TL-VGG16 | 2019 | 97.77% |
Loh et al. [55] | Mask R-CNN | 2021 | 94.57% |
Sağlam et al. [56] | FPGA CNN | 2019 | 94.7% |
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Siłka, W.; Wieczorek, M.; Siłka, J.; Woźniak, M. Malaria Detection Using Advanced Deep Learning Architecture. Sensors 2023, 23, 1501. https://doi.org/10.3390/s23031501
Siłka W, Wieczorek M, Siłka J, Woźniak M. Malaria Detection Using Advanced Deep Learning Architecture. Sensors. 2023; 23(3):1501. https://doi.org/10.3390/s23031501
Chicago/Turabian StyleSiłka, Wojciech, Michał Wieczorek, Jakub Siłka, and Marcin Woźniak. 2023. "Malaria Detection Using Advanced Deep Learning Architecture" Sensors 23, no. 3: 1501. https://doi.org/10.3390/s23031501