A novel technique for detecting suspicious lesions in breast ultrasound images
Bibliographic record
Abstract
Summary We present a new method for automatic detection of suspicious breast cancer lesions using ultrasound. The system is fully automated. It uses fuzzy logic and compounding for de‐noising. A fuzzy membership function based on the gray values of ultrasound images is applied for de‐noising, improving the quality of the image and increasing separation between foreground and background, thus making easier detection of lesions. A novel approach based on neural network is used for segmentation of ultrasound images, and correlation between ultrasound images taken from different angles allows overcoming the problem of shadowing. We consider a combination of morphological and texture features and use sequential forward search, sequential backward search, and distance‐based method to select the best subset of features. We rank the features using distance‐based method and use a combination of sequential forward search and sequential backward search to select the best features (bidirectional search). Finally, support vector machine classifier is used for detecting suspicious lesions. The results of experiments show that our system performs better than other state‐of‐the‐art computer‐aided diagnosis systems with the accuracy of 98.75%. Furthermore, we used concurrency to improve the computational efficiency. In concurrent implementation of de‐noising, segmentation, and feature selection and extraction, we assign each pixel of an ultrasound image to a different thread. We also benefit from multi‐core computing by running each classifier on a different thread. Concurrent implementation of our computer‐aided diagnosis system reduces overall computational time by 85%. Copyright © 2015 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".