Adaptive Attention-Based U-Transnet Architecture with Optimized Metaheuristic Classifier for Early Liver Tumor Detection
Bibliographic record
Abstract
The work presented here gives a novel approach for detecting liver tumours from medical images.The proposed approach is the combination of the latest segmentation and classification techniques that results in early detection of liver tumours with higher accuracy.The methodology applies three phases, the first is the application of anisotropic diffusion filtering that enhances the image quality without disturbing the structural information.After filtering, the second phase makes use of the U-Net architecture and attention-based transformers (U-TransNet) segmentation model for precisely detecting the boundary delineation and tumour detection.The results show Intersection over Union (IoU) as 98%, and dice scores are 99.01%.The third phase in the proposed method applies a support vector machine optimised using a bio-inspired whale optimisation algorithm.The results measured using parameters like accuracy, recall, precision, and F1-score are close to 99%, considering class imbalance effectively in early stages.Comparative analysis in this study validated that the performance of this is better in high-end and advanced methods in segmentation as well as classification techniques.The proposed system has better performance over existing and therefore has a potential of accurate diagnostic of liver tumors for proper treatment of patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".