Semantics Convolutional Neural Network for Medical Images Analysis
Bibliographic record
Abstract
Big Data Analysis is a solution that makes it possible to extract valuable information from the mass of data by using deep learning algorithms and especially the Convolutional Neural Network algorithm. In this article, we have proposed an approach that allows the addition of the semantic aspect in the classification layer of the Convolutional Neural Network algorithm. The proposed approach helps medical professionals to develop an automatic system for identifying various classes of lung cancers. First, the input data are processed to reduce the search space, and the image noise, and normalize data. Then, the preprocessed data are analyzed to reduce image space by preserving all important features. After that, the semantic memory method converts the feature vectors from the analysis layer into semantic feature vectors. Finally, the last layer classifies the input image into two classes. We evaluate our approach using the LUNA16 dataset. Our study led to better results and predictions by reducing false negatives and positives using the Semantic Convolutional Neural Network algorithm. In our approach, cancer tissues can be identified with a maximum of 97.27% for accuracy and 99.46% for AUC. This model has increased efficiency compared with state-of-the-art approaches.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".