Abstract
Abstract We compare Deep Convolutional Neural Networks (DCNN) frameworks, namely AlexNet and VGGNet, for the classification of healthy and malaria-infected cells in large, grayscale, low quality and low resolution microscopic images, in the case only a small training set is available. Experimental results deliver promising results on the path to quick, automatic and precise classification in unstrained images.
| Original language | English |
|---|---|
| DOIs | |
| Publication status | Published - 1 Jan 2019 |
| Externally published | Yes |
| Event | IMVIP 2019: Irish Machine Vision & Image Processing - Technological University Dublin, Dublin, Ireland Duration: 28 Aug 2019 → 30 Aug 2019 |
Conference
| Conference | IMVIP 2019: Irish Machine Vision & Image Processing |
|---|---|
| Country/Territory | Ireland |
| City | Dublin |
| Period | 28/08/19 → 30/08/19 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Deep Convolutional Neural Networks
- DCNN
- AlexNet
- VGGNet
- classification
- malaria-infected cells
- microscopic images
- small training set
- automatic classification
- precise classification
- unstrained images
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