Mid-Upper Arm Circumference Measurement Using Digital Images: A Top-Down Approach with Panoptic Segmentation Using Mask R-CNN

Assessing nutritional status, particularly among children and pregnant women, necessitates accurate measurement of Mid-Upper Arm Circumference (MUAC). This research introduces a novel system for MUAC estimation from digital images using the Mask R-CNN algorithm, employing a top-down panoptic segmentation strategy. The proposed model was designed to identify the upper arm region within human body images and compute MUAC values autonomously. Mask R-CNN was selected due to its capacity to perform precise segmentation of objects within visually complex scenes, especially in the mid-upper arm area. Model training was conducted using a dataset of annotated images, with subsequent evaluation confirming its ability to reliably detect and measure MUAC. The system was validated using 72 image samples, yielding a mean absolute error (MAE) of 2.31 cm when compared to manual measurements. Among these samples, 29.2% (21 individuals) exhibited a measurement discrepancy of 0 to 1 cm, 27.8% (20 individuals) showed a 1 to 2 cm difference, and 43.1% (31 individuals) demonstrated deviations exceeding 2 cm. Despite some variations in measurement accuracy, the system presents a promising tool for enhancing the automation and efficiency of nutritional assessments.

@article{Lydia2025,
  title     = {Mid-Upper Arm Circumference Measurement Using Digital Images: A Top-Down Approach with Panoptic Segmentation Using Mask R-CNN},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Maya Silvi Lydia and Pauzi Ibrahim Nainggolan and Desilia Selvida and Doli Aulia Hamdalah and Dhani Syahputra Bukit and Amalia and Rahmita Wirza Binti O. K. Rahmat},
  doi       = {10.14569/IJACSA.2025.0160839},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160839}
}