A dataset of blood slide images for AI-based diagnosis of malariaDataverse
Malaria is a major public health challenge in sub-Saharan Africa. Timely and accurate diagnosis of malaria is vital to reduce the caseload and mortality rates associated with malaria. The use of microscopy in malaria screening is the gold standard recommended method by the World Health Organisation...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Data in Brief |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340924011521 |
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| author | Rose Nakasi Joyce Nakatumba Nabende Jeremy Francis Tusubira Aloyzius Lubowa Bamundaga Alfred Andama |
| author_facet | Rose Nakasi Joyce Nakatumba Nabende Jeremy Francis Tusubira Aloyzius Lubowa Bamundaga Alfred Andama |
| author_sort | Rose Nakasi |
| collection | DOAJ |
| description | Malaria is a major public health challenge in sub-Saharan Africa. Timely and accurate diagnosis of malaria is vital to reduce the caseload and mortality rates associated with malaria. The use of microscopy in malaria screening is the gold standard recommended method by the World Health Organisation (WHO). In Uganda, utilization of microscopy is challenged by insufficient expertise to interpret the images accurately, affecting the efficiency, effectiveness and accuracy of malaria detection and diagnosis. We present a benchmark dataset of thick and thin blood smear images for automatic malaria screening in Uganda. Mobile Microscopy data was collected from Mulago Hospital, Department of Internal Medicine, Makerere University and Kiruddu National Referral Hospital in Uganda. The labelled image data can be used to build computational models implemented with convolution neural networks. The dataset has 3000 labelled thick blood smear images and 1000 labelled thin blood smear images. The datasets will support robust and accurate deep learning models for malaria diagnosis using thick and thin blood smear images with reasonable detection accuracies. |
| format | Article |
| id | doaj-art-655c32ecb89a4ca996fd32d12db12507 |
| institution | Kabale University |
| issn | 2352-3409 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj-art-655c32ecb89a4ca996fd32d12db125072025-01-31T05:11:27ZengElsevierData in Brief2352-34092025-02-0158111190A dataset of blood slide images for AI-based diagnosis of malariaDataverseRose Nakasi0Joyce Nakatumba Nabende1Jeremy Francis Tusubira2Aloyzius Lubowa Bamundaga3Alfred Andama4Makerere University, P.O Box 7062, Kampala, Uganda; Corresponding author.Makerere University, P.O Box 7062, Kampala, UgandaMakerere University, P.O Box 7062, Kampala, UgandaMakerere University, P.O Box 7062, Kampala, UgandaMulago National Referral Hospital, P.O Box 7051, Kampala, UgandaMalaria is a major public health challenge in sub-Saharan Africa. Timely and accurate diagnosis of malaria is vital to reduce the caseload and mortality rates associated with malaria. The use of microscopy in malaria screening is the gold standard recommended method by the World Health Organisation (WHO). In Uganda, utilization of microscopy is challenged by insufficient expertise to interpret the images accurately, affecting the efficiency, effectiveness and accuracy of malaria detection and diagnosis. We present a benchmark dataset of thick and thin blood smear images for automatic malaria screening in Uganda. Mobile Microscopy data was collected from Mulago Hospital, Department of Internal Medicine, Makerere University and Kiruddu National Referral Hospital in Uganda. The labelled image data can be used to build computational models implemented with convolution neural networks. The dataset has 3000 labelled thick blood smear images and 1000 labelled thin blood smear images. The datasets will support robust and accurate deep learning models for malaria diagnosis using thick and thin blood smear images with reasonable detection accuracies.http://www.sciencedirect.com/science/article/pii/S2352340924011521Machine learningComputer visionMalaria microscopy |
| spellingShingle | Rose Nakasi Joyce Nakatumba Nabende Jeremy Francis Tusubira Aloyzius Lubowa Bamundaga Alfred Andama A dataset of blood slide images for AI-based diagnosis of malariaDataverse Data in Brief Machine learning Computer vision Malaria microscopy |
| title | A dataset of blood slide images for AI-based diagnosis of malariaDataverse |
| title_full | A dataset of blood slide images for AI-based diagnosis of malariaDataverse |
| title_fullStr | A dataset of blood slide images for AI-based diagnosis of malariaDataverse |
| title_full_unstemmed | A dataset of blood slide images for AI-based diagnosis of malariaDataverse |
| title_short | A dataset of blood slide images for AI-based diagnosis of malariaDataverse |
| title_sort | dataset of blood slide images for ai based diagnosis of malariadataverse |
| topic | Machine learning Computer vision Malaria microscopy |
| url | http://www.sciencedirect.com/science/article/pii/S2352340924011521 |
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