AUTOMATED DETECTION OF CHILDHOOD OBESITY IN ABDOMINOPELVIC REGION USING THERMAL IMAGING BASED ON DEEP LEARNING TECHNIQUES
Bibliographic record
Abstract
Childhood obesity is a preventable disorder which can reduce the risk of the comorbidities linked with an adult obesity. In order to improve the lifestyle of the obese children, early and accurate detection is required by using some non-invasive technique. Thermal imaging helps in evaluation of childhood obesity without injecting any form of harmful radiation in human body. The goal of this proposed research is to evaluate the body surface temperature in abdominopelvic and cervical regions and to evaluate which region is best for predicting childhood obesity using thermal imaging. Next, to customize the ResNet-18 and VGG-19 architecture using transfer learning approach and to obtain the best modified classifier and to study the classification accuracy between normal and obese children. The two-study region which was selected for this study was abdominopelvic and cervical region where the mean skin surface temperature was recorded. From the two selected body regions, abdominopelvic region has depicted highest temperature difference of 10.98% between normal and obese subjects. The proposed modified ResNet-18 model produced an overall accuracy of 94.2% than the modified VGG-19 model (86.5%) for the classification of obese and normal children. Thus, this study can be considered as a non-invasive and cost-effective way for pre-screening the obesity condition in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".