ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements
Precisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervis...
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MDPI AG
2025-04-01
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| Series: | Journal of Imaging |
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| author | Hezha H. MohammedKhan Cascha Van Wanrooij Eric O. Postma Çiçek Güven Marleen Balvert Heersh Raof Saeed Chenar Omer Ali Al Jaf |
| author_facet | Hezha H. MohammedKhan Cascha Van Wanrooij Eric O. Postma Çiçek Güven Marleen Balvert Heersh Raof Saeed Chenar Omer Ali Al Jaf |
| author_sort | Hezha H. MohammedKhan |
| collection | DOAJ |
| description | Precisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the “Age-Restricted Anonymized” (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child’s height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children’s growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals. |
| format | Article |
| id | doaj-art-9b2beb30240f4f6eba697ba422a17bfa |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-9b2beb30240f4f6eba697ba422a17bfa2025-08-20T03:47:59ZengMDPI AGJournal of Imaging2313-433X2025-04-0111514210.3390/jimaging11050142ARAN: Age-Restricted Anonymized Dataset of Children Images and Body MeasurementsHezha H. MohammedKhan0Cascha Van Wanrooij1Eric O. Postma2Çiçek Güven3Marleen Balvert4Heersh Raof Saeed5Chenar Omer Ali Al Jaf6Zero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The NetherlandsZero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The NetherlandsDepartment of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The NetherlandsDepartment of Cognitive Science and Artificial Intelligence, Tilburg School of Humanities and Digital Sciences, Tilburg University, 5037 AB Tilburg, The NetherlandsZero Hunger Lab, Department of Econometrics & Operations Research, Tilburg School of Economics and Management, Tilburg University, 5037 AB Tilburg, The NetherlandsCollege of Medicine, University of Sulaimani, Kurdistan Region, Sulaymaniyah 46002, IraqCollege of Medicine, University of Sulaimani, Kurdistan Region, Sulaymaniyah 46002, IraqPrecisely estimating a child’s body measurements and weight from a single image is useful in pediatrics for monitoring growth and detecting early signs of malnutrition. The development of estimation models for this task is hampered by the unavailability of a labeled image dataset to support supervised learning. This paper introduces the “Age-Restricted Anonymized” (ARAN) dataset, the first labeled image dataset of children with body measurements approved by an ethics committee under the European General Data Protection Regulation guidelines. The ARAN dataset consists of images of 512 children aged 16 to 98 months, each captured from four different viewpoints, i.e., 2048 images in total. The dataset is anonymized manually on the spot through a face mask and includes each child’s height, weight, age, waist circumference, and head circumference measurements. The dataset is a solid foundation for developing prediction models for various tasks related to these measurements; it addresses the gap in computer vision tasks related to body measurements as it is significantly larger than any other comparable dataset of children, along with diverse viewpoints. To create a suitable reference, we trained state-of-the-art deep learning algorithms on the ARAN dataset to predict body measurements from the images. The best results are obtained by a DenseNet121 model achieving competitive estimates for the body measurements, outperforming state-of-the-art results on similar tasks. The ARAN dataset is developed as part of a collaboration to create a mobile app to measure children’s growth and detect early signs of malnutrition, contributing to the United Nations Sustainable Development Goals.https://www.mdpi.com/2313-433X/11/5/142image-based body shape estimationconvolutional neural networksdataset |
| spellingShingle | Hezha H. MohammedKhan Cascha Van Wanrooij Eric O. Postma Çiçek Güven Marleen Balvert Heersh Raof Saeed Chenar Omer Ali Al Jaf ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements Journal of Imaging image-based body shape estimation convolutional neural networks dataset |
| title | ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements |
| title_full | ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements |
| title_fullStr | ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements |
| title_full_unstemmed | ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements |
| title_short | ARAN: Age-Restricted Anonymized Dataset of Children Images and Body Measurements |
| title_sort | aran age restricted anonymized dataset of children images and body measurements |
| topic | image-based body shape estimation convolutional neural networks dataset |
| url | https://www.mdpi.com/2313-433X/11/5/142 |
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