Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach
Abstract Anaemia has become one of the biggest burdens to public health globally. It affects billions of people, particularly in underprivileged communities in developing countries, due to inadequate diets, such as low intake of iron or vitamins. This deficiency affects the cognitive development and...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00238-5 |
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| author | Justice Williams Asare Martin Mabeifam Ujakpa Seth Alornyo Emmanuel Akwah Kyei Prince Modey William Leslie Brown-Acquaye Emmanuel Freeman Alfred Coleman |
| author_facet | Justice Williams Asare Martin Mabeifam Ujakpa Seth Alornyo Emmanuel Akwah Kyei Prince Modey William Leslie Brown-Acquaye Emmanuel Freeman Alfred Coleman |
| author_sort | Justice Williams Asare |
| collection | DOAJ |
| description | Abstract Anaemia has become one of the biggest burdens to public health globally. It affects billions of people, particularly in underprivileged communities in developing countries, due to inadequate diets, such as low intake of iron or vitamins. This deficiency affects the cognitive development and psychological growth of children. This study aims to detect anaemia in children aged 6–59 months using images of the conjunctiva of the eye, employing single convolutional neural network (CNN) architectures, including AlexNet, DenseNet, and VGGNet. These CNN architectures were combined to create a multi-architecture model by using the feature concatenation technique, which outperformed the individual architectures, achieving an accuracy of 98.79%. In comparison, AlexNet, VGGNet, and DenseNet achieved an accuracy of 96.76%, 93.45%, and 94.21%, respectively. The results of this study demonstrate that integrating an advanced CNN multiarchitecture for anaemia detection represents a significant advancement in the healthcare sector. It enhances the accuracy, efficiency, and accessibility of medical disease detection while being cost-effective and scalable, providing quicker results in healthcare screening compared to traditional methods. |
| format | Article |
| id | doaj-art-e075dfd22c614c78812afb8429613f0f |
| institution | DOAJ |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-e075dfd22c614c78812afb8429613f0f2025-08-20T03:04:22ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-07-0112112510.1186/s43067-025-00238-5Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approachJustice Williams Asare0Martin Mabeifam Ujakpa1Seth Alornyo2Emmanuel Akwah Kyei3Prince Modey4William Leslie Brown-Acquaye5Emmanuel Freeman6Alfred Coleman7Faculty of Applied Science and Technology, Koforidua Technical UniversitySchool of Management, IT and Governance, University of KwaZulu-NatalFaculty of Applied Science and Technology, Koforidua Technical UniversityFaculty of Computing and Information Systems, Ghana Communication Technology UniversityFaculty of Computing and Information Systems, Ghana Communication Technology UniversityFaculty of Computing and Information Systems, Ghana Communication Technology UniversityFaculty of Computing and Information Systems, Ghana Communication Technology UniversityFaculty of Computing and Information Systems, Ghana Communication Technology UniversityAbstract Anaemia has become one of the biggest burdens to public health globally. It affects billions of people, particularly in underprivileged communities in developing countries, due to inadequate diets, such as low intake of iron or vitamins. This deficiency affects the cognitive development and psychological growth of children. This study aims to detect anaemia in children aged 6–59 months using images of the conjunctiva of the eye, employing single convolutional neural network (CNN) architectures, including AlexNet, DenseNet, and VGGNet. These CNN architectures were combined to create a multi-architecture model by using the feature concatenation technique, which outperformed the individual architectures, achieving an accuracy of 98.79%. In comparison, AlexNet, VGGNet, and DenseNet achieved an accuracy of 96.76%, 93.45%, and 94.21%, respectively. The results of this study demonstrate that integrating an advanced CNN multiarchitecture for anaemia detection represents a significant advancement in the healthcare sector. It enhances the accuracy, efficiency, and accessibility of medical disease detection while being cost-effective and scalable, providing quicker results in healthcare screening compared to traditional methods.https://doi.org/10.1186/s43067-025-00238-5AnaemiaConjunctivaMultiarchitectureDetectionFeature concatenation technique |
| spellingShingle | Justice Williams Asare Martin Mabeifam Ujakpa Seth Alornyo Emmanuel Akwah Kyei Prince Modey William Leslie Brown-Acquaye Emmanuel Freeman Alfred Coleman Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach Journal of Electrical Systems and Information Technology Anaemia Conjunctiva Multiarchitecture Detection Feature concatenation technique |
| title | Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach |
| title_full | Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach |
| title_fullStr | Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach |
| title_full_unstemmed | Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach |
| title_short | Anaemia detection in infants of 6–59 months using medical images: a multi-architecture convolutional neural network-based approach |
| title_sort | anaemia detection in infants of 6 59 months using medical images a multi architecture convolutional neural network based approach |
| topic | Anaemia Conjunctiva Multiarchitecture Detection Feature concatenation technique |
| url | https://doi.org/10.1186/s43067-025-00238-5 |
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