FSCNN: Fuzzy Channel Filter-Based Separable Convolution Neural Networks for Medical Imaging Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Intraclass heterogeneity of medical diagnostic objects poses a challenge for accurate intraclass classification of medical fine-grained images (MFGIs) within deep learning. To accurately classify MFGIs, we propose a novel approach termed fuzzy channel filter-based separable convolution neural networks (FSCNN). The original design of FSCNN comprises the following components: 1) Designing the fuzzy channel filter (FCF) module, devised to establish long-distance feature dependencies for each feature channel with the input image by formulating fuzzy rules “IF–THEN”. 2) The FCF-based separable convolution (FSC) block uses depth-wise and point-wise convolutions to extract and mix feature channels. Then, the internal information of each feature channel is reintegrated through fuzzy weighted averaging in FCF to enhance fine-grained feature information. 3) Creating the deep fuzzy learning architecture FSCNN through the superimposition of FSC blocks. This architectural arrangement enables more effective learning of fine-grained feature distinctions within MFGIs, thereby enhancing classification accuracy. Compared to other advanced fine-grained classification models, including state-of-the-art models, our model outperforms by 2%–6% and 3%–9% on brain MRI and pneumonia CT datasets, respectively.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it