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- W3112485562 abstract "Identifying patients, infected with the virulent disease, malaria requires a reliable and quick diagnosis of blood cells. This paper presents a computer-aided diagnosing (CADx) method supported by a deep convolutional neural network (CNN) for assisting clinicians to detect malaria by medical image. We employed the VGG-19 and ResNet-50 architectures to create several models for two types of study (parasitized and uninfected erythrocytes). To enhance the model’s performance, an ensemble technique was applied, followed by which, the best model selected by performance measuring metrics. Our proposed model was qualified and examined upon a standard microscopic set of images collected from the National Institute of Health (NIH). The final result was analogized with other techniques, where the accuracy of this model was 96.7% for patient-level detection. To resolute the limitations and minimizing errors regarding automated malaria detection, the proposed model proved to be an appropriate strategy for distant regions and emergencies." @default.
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- W3112485562 date "2020-12-17" @default.
- W3112485562 modified "2023-10-04" @default.
- W3112485562 title "Deep CNN-Supported Ensemble CADx Architecture to Diagnose Malaria by Medical Image" @default.
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- W3112485562 doi "https://doi.org/10.1007/978-981-33-4673-4_20" @default.
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