Single-Level Fusion for Enhancing Meat Quality Classification with Explainable AI
Bibliographic record
Abstract
Growing the global meat market, especially in the Asia-Pacific region, also brought a desire to continue providing quality meats, and this implies the need to invest in a high-quality meat supply to avoid health issues associated with spoiled meat like stomach upsets and food poisoning. This study proposes a state-of-the-art deep-learning model of a single fusion level of ConvNeXtTiny and DenseNet169 networks for categorizing meat as fresh, half-fresh, and spoiled. Pivotal to this study is the use of Grad-CAM++ for explainable AI (XAI) that offers finer localization by generating high-resolution heatmaps that focus on the important regions in images. This approach enhances the interpretability of the model’s decision-making process in a way that enables users to determine which of the features contributes to classification results easily. The model, trained on a comprehensive dataset of 10,931 images, achieved a notable testing accuracy of 97.46%, outperforming recent studies. Therefore, besides demonstrating the effectiveness of the chosen XAI techniques, this study contributes to enhancing the safety and quality of meat products by providing insightful explanations of the operations of the AI system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".