Enhanced Speckle Noise Reduction in Breast Cancer Ultrasound Imagery Using a Hybrid Deep Learning Model
Bibliographic record
Abstract
Ultrasonic imaging serves as a pivotal tool in mitigating overdiagnosis of breast cancer in women, owing to its high sensitivity, low false-positive rate, and ability to reduce unnecessary biopsies. Nevertheless, these images are impaired by speckle noise, which appears as granular interference obscuring tissue boundaries and diminishing image contrast. This noise impedes subsequent image processing tasks such as edge detection, segmentation, feature extraction, and classification. Existing strategies for speckle noise reduction in ultrasonic images either compromise on effectiveness or demand substantial processing time, presenting challenges in preserving fine edge details. Addressing these issues, we propose an innovative hybrid deep learning model, FCNN-IDOA, which synergizes a Fundamental Convolutional Neural Network (FCNN) with an optimization algorithm. Our FCNN model is built upon the framework of GoogLeNet, enhanced with fifteen additional layers to augment its expressiveness. Subsequently, an Improved Dragonfly Optimization Algorithm (IDOA) is deployed to optimize FCNN's parameters, thereby improving the computational efficiency of the model. The suggested model has demonstrated superior performance, outstripping previous models in terms of accuracy. During experimental validation, the model achieved an average t(s) value of 84.764421, a PSNR value of 66, an MSE value of 54.9143, an RMSE value of 0.491631, and a final t(s) value of 83.759067. The results indicate that this novel model significantly outperforms the BC models, rendering it a promising solution for speckle noise reduction in breast cancer ultrasound images.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".