Enhancing Medical Diagnosis through Multimodal Image Fusion: A Novel Approach Using Modified Swin-Based Cross Attention Fusion
Bibliographic record
Abstract
In recent times, the multimodal medical image fusion technique has emerged as a most promising area of medical diagnosis. To effectively merge the details of the medical image without losing any information is the major challenge. This work proposes a novel Modified Swin-based Cross Attention Fusion framework for effectively fuzing multimodal medical images. The Fuzzy Sets are deployed to assess the image quality and remove uncertainties. The modified Visual Geometry Group19 and Attention-based Convolutional Neural Network models are deployed to extract the deep features from the preprocessed images. The Modified Visual Geometry Group19 utilizes a Gaussian Error Linear Unit and Maxpooling, which extracts the spatial features and mitigates the complexity. The Attention-based Convolutional Neural Network employs a channel attention squeeze and excitation for learning the feature weight to improve the feature extraction. Further, the Swin-based Cross Attention Fusion model is employed for fuzing the images that aggregate the features of intra-domain and inter-domain global context, followed by a Transformer-based deep feature reconstruction unit and Convolutional Neural Network-based medical image reconstruction unit produces the final fused image. The experimental analysis confirms that the model achieved a higher Fusion Factor of 8.93, which indicates its significance in the fusion process.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".