营养不良检测的深度学习方法

Shilpa Ankalaki, Vidyadevi G Biradar, Kishore Kumar Naik P, Geetabai S. Hukkeri
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引用次数: 0

摘要

及时发现儿童营养不良至关重要,因为这有助于及早干预和治疗。这种积极主动的方法不仅能防止健康状况进一步恶化,还能促进正常生长,最大限度地减少营养不良造成的长期后果,如生长迟缓、认知发展受损和更易患病等。我们的工作包括创建一个新的数据集,其中包括健康、营养不良、发育迟缓和消瘦类别的儿童图像。核心目标是评估深度学习模型在对这些儿童图像进行分类时的性能。在模型训练过程中,通过改变epochs、批量大小、优化器AdamW、Adamax和RMSprop以及学习率0.1、0.01、0.001和0.0001的不同值,进行了实验。模型在由稳定扩散模型生成的图像清洗后构建的图像数据集上进行训练。模型在从网站随机选取的儿童图像上进行了测试。在使用 Adam 优化器、0.0001 学习率和批量大小为 4 的情况下,该模型成功地对两个类别进行了分类,准确率为 95%,F1 分数为 97.6%,精确率为 97.6%,召回率为 97.6%。此外,针对四类分类场景,研究扩大了分类范围。在使用 AdamW 优化器、0.0001 学习率和批量大小为 6 的情况下,该模型在四类分类中取得了 88.87% 的准确率、90.3% 的召回率、90.2% 的精确率和 90% 的 F1 分数。这些结果对于预测儿童营养不良类别是令人满意的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Learning Approach for Malnutrition Detection
The timely detection of malnutrition in children is of paramount importance, as it allows for early intervention and treatment. This proactive approach not only prevents further health deterioration but also fosters proper growth, minimizing the long-term consequences of malnutrition, such as stunted growth, impaired cognitive development, and increased vulnerability to diseases. Our work encompasses the creation of a new dataset comprising images of children in Healthy, Undernourished, Stunting, and Wasting categories. The core objective is to assess the deep learning model performance in classifying these children images. The experimentation is carried out by varying epochs, batch size, optimizers AdamW, Adamax, and RMSprop; and different values of the learning rate 0.1, 0.01, 0.001, and 0.0001 during model training. The model is trained on image dataset constructed by cleaning images generated by the stable diffusion model. The model is tested on randomly selected child images from websites. The model successfully classified two classes with 95% accuracy, 97.6% F1 score, precision 97.6%, and 97.6% recall with Adam optimizers, 0.0001 learning rate, and Batch size 4. Additionally, for the four-class categorization scenario, the study broadens the classification. The model achieved 88.87% accuracy, 90.3% recall, 90.2% precision, and an F1 score of 90% for four-class categorization with AdamW optimization, 0.0001 learning rate, and batch size 6. These results are satisfactory for prediction of malnutrition category in children.
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