ABRMS-Net: An Attention-Based Residual Multi-Scale CNN for image classification
Bibliographic record
Abstract
Despite the widespread application of neural networks in deep learning for image classification, challenges persist in accurately recognizing images, especially complex structures such as brain tumors. We proposed the Attention-Based Residual Multiscale Convolutional Neural Network (ABRMS-Net), which integrates four attention mechanisms to capture multi-scale features and subtle differences in images. We compared ABRMS-Net with eight popular convolutional neural networks (CNNs) across two datasets. Experimental results indicate that our model outperforms these eight CNNs, demonstrating exceptional performance, including achieving the best classification accuracy of 99.47 ± 0.39% in the brain tumor dataset and exceeding the baseline by over 21.58% in the evaluation metrics in the face expression dataset. Additionally, through heatmap visualization, this paper demonstrates the interpretability of ABRMS-net within neural networks and its effectiveness in using a small number of training samples for the development of few-shot learning. The code will be made publicly accessible at https://github.com/AIPMLab/ABRMS-Net.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".