Malaria detection using deep residual networks with mobile microscopy Article Swipe
Priyadarshini Adyasha Pattanaik
,
Mohit Mittal
,
Mohammad Zubair Khan
,
Surya Narayan Panda
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.jksuci.2020.07.003
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1016/j.jksuci.2020.07.003
Automatic segmentation of erythrocytes in microscopic blood smear phone images is a critical step to visualize and identify malaria using machine learning technologies. However, it still remains a challenging problem due to the scarcity of experts, low image qualities, slow manual and inefficient quality of diagnosis. To handle these issues to some extent, we proposed an effective multi-magnification deep residual neural network (MM-ResNet), where we fully automatically classify the microscopic blood smear images as either infected/ non-infected at multiple magnifications. We have experimentally evaluated our approach by using it to train more efficient variants of different compact deep convolutional neural networks (CNN), evaluated on phone datasets. The MM-ResNet end-to-end framework shows similar or superior accuracy than the baseline architectures, as measured by GPU timings on the publicly available microscopic blood smear phone images. This approach is the first application of a MM-ResNet for malaria-infected erythrocyte identification in microscopic blood smear images.
Related Topics
Concepts
Convolutional neural network
Computer science
Artificial intelligence
Residual
Deep learning
Residual neural network
Blood smear
Mobile phone
Segmentation
Magnification
Pattern recognition (psychology)
Malaria
Computer vision
Phone
Pathology
Medicine
Telecommunications
Linguistics
Philosophy
Algorithm
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.jksuci.2020.07.003
- OA Status
- hybrid
- Cited By
- 58
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3043424264
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3043424264Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.jksuci.2020.07.003Digital Object Identifier
- Title
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Malaria detection using deep residual networks with mobile microscopyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-07-16Full publication date if available
- Authors
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Priyadarshini Adyasha Pattanaik, Mohit Mittal, Mohammad Zubair Khan, Surya Narayan PandaList of authors in order
- Landing page
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https://doi.org/10.1016/j.jksuci.2020.07.003Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.jksuci.2020.07.003Direct OA link when available
- Concepts
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Convolutional neural network, Computer science, Artificial intelligence, Residual, Deep learning, Residual neural network, Blood smear, Mobile phone, Segmentation, Magnification, Pattern recognition (psychology), Malaria, Computer vision, Phone, Pathology, Medicine, Telecommunications, Linguistics, Philosophy, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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58Total citation count in OpenAlex
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2025: 5, 2024: 16, 2023: 10, 2022: 16, 2021: 10Per-year citation counts (last 5 years)
- References (count)
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38Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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