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About Malaria Detection Using Deep Convolutional Neural Networks: ISSN-2394-5125
The document summarizes research on using a deep convolutional neural network (CNN) to detect malaria from digitized microscopic images of red blood cells. Key points: 1) A CNN was trained on a dataset of 32,353 segmented red blood cell images from malaria-infected patients to classify cells as infected or uninfected. 2) Various preprocessing techniques were applied to the images including min-max normalization and data augmentation to improve the CNN's performance. 3) The proposed CNN approach achieved 97.9% accuracy and 96.2% sensitivity in detecting malaria, outperforming previous methods that relied on manual examination or other machine learning models.
Detailed Information
| Author: | ['GEORGE'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 9 |
| Language: | English |
| Format: | |
| Price: | FREE |
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