Leveraging Deep Learning for Anemia Detection Using Palm Images: Innovative Solutions for Sustainable Development

Abstract

A wide variety of medical diagnoses depend on the study and testing of the dis-ease images. Anemia is one of the cases separated in the populations. Deep Learning (DL) is one of the subfields of artificial intelligence (AI) techniques applied in the healthcare system to diagnose diseases. In this work, YOLOv11 with its versions Nano (n), Small (s), Medium (m), Large (l), and Extra-large (x) produces deep-learning models to detect anemia based on the palm images. The models train on 4260 color images of children under 5 years labeled anemic and nonanemic. The models train using two different dataset splits, with two input image sizes, (64x64) and (128x128). The high training accuracy achieves at YOLOv11n 99.3% at the group two dataset with an input image size of 128 and YOLOv11n 98.9% at the group one dataset with an input image size of 128. All models are test, and the best test results obtains with the Yolov11n models, 98.9% and 99.5% of the two groups at input image size 128, which predicted correctly with a high percentage in a very short time. We produce these models to assess medical images, providing precise automated estimations and reducing diagnosis time and errors. Additionally, they help reduce death rates and promote early intervention to enhance the quality of life for patients.