Classification of Ultrasound Breast Images Using Fused Ensemble of Deep Learning Classifiers
Bibliographic record
Abstract
Ultrasound (US) imaging is an affordable, radiation-free screening that has been successfully used for early stage breast cancer screening. Deep learning-based classifiers are currently being used to classify breast cancer. Deep learning requires large amount of dataset for training. However, currently available databases of breast cancer US images are small and the images have tumors of different sizes. Therefore, the deep learning-based classifiers are unable to provide good generalization. To address these challenges, we propose a fusion of three models namely transfer learning, multi-scale and autoencoder. Transfer learning model is based on VGG16 and is used to overcome the issue of limited data. Convolutional autoencoders extract features that can represent even noisy images. We propose a novel multi-scale deep learning model to address learning of US images with tumors of various sizes and shapes. These three models are trained independently and then their classification outputs are fused using differential evolution (DE) algorithm to get the final classification results. The proposed novel fused ensemble of deep learning-based classifiers is evaluated using two publicly available US datasets. Transfer learning, autoencoder, and multi-scale models individually achieve an accuracy of 88%, 85%, and 89% respectively. The fusion of the outputs of the three models using DE algorithm provides a classification accuracy with an accuracy of 93%. The source code available at https://github.com/EbrahimAli1989/Breast-Cancer-classification-.git.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".