Deep Learning for Malaria Diagnosis: Leveraging Convolutional Neural Networks for Accurate Parasite Detection
malaria is one of the most severe diseases worldwide. However, the current diagnostic method that involves examining blood smears under a microscope is unreliable and heavily relies on the examiner's expertise. Recent attempts to use deep-learning algorithms for malaria diagnosis have not prod...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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College of Education for Pure Sciences
2023-09-01
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| Series: | Wasit Journal for Pure Sciences |
| Online Access: | https://wjps.uowasit.edu.iq/index.php/wjps/article/view/205 |
| Summary: | malaria is one of the most severe diseases worldwide. However, the current diagnostic method that involves examining blood smears under a microscope is unreliable and heavily relies on the examiner's expertise. Recent attempts to use deep-learning algorithms for malaria diagnosis have not produced satisfactory results. But, a new CNN-based machine learning model has been proposed in a research paper that can automatically detect and predict infected cells in thin blood smears with 94.63% accuracy. This model accurately accentuates the region of interest for the stained parasite in the images, which increases its reliability, transparency, and comprehensibility, making it suitable for deployment in healthcare settings.
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| ISSN: | 2790-5233 2790-5241 |