Automated detection of lung cancer using statistical and morphological image processing techniques
Bibliographic record
Abstract
Lung cancer represents the second most commonly diagnosed cancer among Jordanian population. Evidence that early detection of lung cancer may allow for more timely therapeutic intervention has provided the momentum for lung cancer screening programs around the world. In this study, a computer aided detection (CAD) system is proposed in an attempt to detect the lung cancer areas using computed tomography (CT) images. It is implemented as a “second reader” to help radiologists focus their attention on regions that might be missed during visual interpretation. The proposed CAD system has three main stages; Segmentation by thresholding the CT images, labeling the founded regions and then extracting some diagnostic features of each region for further analysis and interpretation. The study is trained, tested, and validated using images obtained from forty five patients. The obtained results perfectly match the radiologist's diagnosis in detecting the defected areas and quantitatively measuring its size, location, borders as well as displaying its other diagnostic characteristics. Moreover, the proposed system can detect misclassified regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".