A combined UNet++ and LSTM approach for breast ultrasound image segmentation
Bibliographic record
Abstract
Breast cancer is one of the most common causes of death among women worldwide. This underscores the critical importance of early detection and accurate segmentation of tumors. Deep learning techniques, particularly UNet and UNet++ architectures, have shown remarkable performance in segmenting breast ultrasound images. However, these models do not capture the temporal features in the images. To address this limitation, this study introduces a novel hybrid architecture that combines the encoder–decoder structure of UNet++ with long short-term memory (LSTM) networks to integrate both spatial and temporal features effectively. Additionally, a multi-scale feature extraction module and self-attention mechanisms have been embedded to enable hierarchical feature fusion for capturing richer contextual information. The proposed end-to-end segmentation pipeline was rigorously evaluated using the BUSI with GT dataset; a publicly available dataset of ultrasound-based breast cancer images with corresponding ground truth masks. The model achieved outstanding performance, with an Accuracy of 98.88%, Specificity of 99.53%, Precision of 95.34%, Sensitivity of 91.20%, F1-Score of 93.74%, and Dice coefficient of 92.74%. These results represent a promising improvement over existing methods, suggesting that the proposed approach could serve as a valuable tool within clinical workflows. This study proposes a model called UNet++ + LSTM for the segmentation. As a modified network of Unet++, the model is mainly composed of three parts: a multi-scale feature extraction module, an attention block, and a multi-task learning module. Additionally, This model can process high-frequency local information to improve segmentation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".