The purposes of the study were (i) to determine the potential of thermal imaging to assess the difference in the thermal pattern in various body regions of studied population; (ii) to compare the performance of feature extraction, feature fusion, feature ranking and feature dimension reduction (PCA) in classification of obese and normal children using different Machine learning algorithms. About 600 thermograms were obtained from various regions such as abdomen, finger bed, forearm, neck, shank and gluteal region for the studied population. Fifteen statistical textual features were extracted from the six regional thermograms followed by implementing feature fusion with SIFT and SURF algorithm. The PCA method provides the best classification accuracy for SVM (98%) followed by Naïve Bayes and Random Forest (97%). Thus, the regional thermography and computer aided diagnostic tool with machine learning classifier could be used as a basic non-invasive prognostic tool for the evaluation of obesity in children.

中文翻译：

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基于机器学习技术的儿童肥胖热成像方法

该研究的目的是 (i) 确定热成像的潜力，以评估研究人群不同身体区域的热模式差异；(ii) 比较特征提取、特征融合、特征排序和特征降维 (PCA) 在使用不同机器学习算法对肥胖和正常儿童进行分类时的性能。从研究人群的腹部、指床、前臂、颈部、小腿和臀部区域等不同区域获得了大约 600 个热谱图。从六个区域热图中提取了十五个统计文本特征，然后用 SIFT 和 SURF 算法实现特征融合。PCA 方法为 SVM 提供了最好的分类准确度 (98%)，其次是朴素贝叶斯和随机森林 (97%)。因此，