Automatic classification of carotid ultrasound images based on convolutional neural network
Bibliographic record
Abstract
Ultrasound imaging has become a routine means of diagnosing atherosclerosis. The classification of carotid ultrasound images and detection for the plaques automatically are critical for the diagnosis of atherosclerosis, which has important clinical significance for further analysis of plaque vulnerability and risk assessment of cardiovascular and cerebrovascular events. At present, manual measurement is used for the classification, which has obvious disadvantages such as inaccurate measurement and operator variability. In this paper, we proposed an automatic classification method based on convolutional neural network (CNN) for the carotid ultrasound images from different research institutions and ultrasound machines. 820 and 830 carotid ultrasound images from Zhongnan Hospital of Wuhan University and Robarts Research Institute of Canada were used for the classification of normal, thickened vessel wall and plaque images. To solve the problem of uneven image quality and size, we used six different image normalization schemes. Furthermore, we designed five CNNs with slightly different structures and compared them with texture-based features classifications. The CNN results showed significant superiority in classification performance with total accuracy of 90.30% and recall rate of 89.70%, indicating the automatic classification of carotid ultrasound images based on CNN is potentially useful for clinical application in the diagnosis of carotid atherosclerosis.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".