Aanjankumar S, Sathyamoorthy Malathy, Dhanaraj Rajesh Kumar, Surjit Kumar S R, Poonkuntran S, Khadidos Adil O, Selvarajan Shitharth
School of Computing Science and Engineering, VIT Bhopal University, Bhopal-Indore Highway, Kothrikalan, 466114, Sehore, Madhya Pradesh, India.
Department of Information Technology, KPR Institute of Engineering and Technology, Coimbatore, India.
Sci Rep. 2025 Mar 6;15(1):7871. doi: 10.1038/s41598-025-91825-z.
In recent times, severe acute malnutrition (SAM) in India is considered a serious issue as per UNICEF 2022 records. In that record, 35.5% of children under age 5 are stunted, 19.3% are wasted, and 32% are underweight. Malnutrition, defined as these three conditions, affects 5.7 million children globally. This research utilizes an artificial intelligence-based image segmentation technique to predict malnutrition in children. The primary goal of this research is to use a deep learning model to eliminate the need for multiple manual diagnostic tests and simplify the prediction of malnutrition in kids. The traditional model uses text-based data and takes more time with continuous monitoring of kids by analysing body mass index (BMI) over different periods. Children in rural areas often miss medical expert appointments, and a lack of knowledge among parents can lead to severe malnutrition. The aim of the proposed system is to eliminate the need for manual blood tests and regular visits to medical experts. This study uses the ResNet-50 deep learning model's built-in shortcut connection to solve the image-based vanishing gradient problem. This makes training more efficient for image segmentation tasks in predicting malnutrition. The model is 98.49% accurate in predicting the kids who are malnourished among the kids who are healthy. It is evident from the results that the proposed system serves better than other deep learning models, such as XG Boost (75.29% accuracy), VGG 16 (94% accuracy), Xception (95.41% accuracy), and MobileNet (92.42% accuracy). Hence, the proposed technique is effective in detecting malnutrition and diagnose it earlier, without using predictive analysis function or advice from the medical experts.
根据联合国儿童基金会2022年的记录，近年来，印度的重度急性营养不良（SAM）被视为一个严重问题。在该记录中，5岁以下儿童中有35.5%发育迟缓，19.3%消瘦，32%体重不足。这三种情况定义的营养不良影响了全球570万儿童。本研究利用基于人工智能的图像分割技术来预测儿童营养不良。这项研究的主要目标是使用深度学习模型，消除多项手动诊断测试的需求，并简化对儿童营养不良的预测。传统模型使用基于文本的数据，通过分析不同时期的体重指数（BMI）对儿童进行持续监测，耗时更长。农村地区的儿童经常错过与医学专家的预约，而且家长缺乏相关知识可能导致严重营养不良。所提出系统的目的是消除手动血液检测和定期拜访医学专家的需求。本研究使用ResNet-50深度学习模型的内置捷径连接来解决基于图像的梯度消失问题。这使得在预测营养不良的图像分割任务中训练更加高效。该模型在预测健康儿童中营养不良儿童方面的准确率为98.49%。结果表明，所提出的系统比其他深度学习模型表现更好，如XG Boost（准确率75.29%）、VGG 16（准确率94%）、Xception（准确率95.41%）和MobileNet（准确率92.42%）。因此，所提出的技术在检测营养不良并更早进行诊断方面是有效的，无需使用预测分析功能或医学专家的建议。
