Detection of Lung Cancer Tumor in CT Scan Images Using Novel Combination of Super Pixel and Active Contour Algorithms
Bibliographic record
Abstract
Lung cancer is a problem that has become increasingly widespread in recent years due to smoking, poor nutrition and other factors. If lung cancer cells are identified at an early stage, they will be crucial in saving lives. Machine learning-based approaches to detecting lung cancer tumors have reduced the need for manpower, reduced human error and reduced medical costs. CT scan images are one of the efficient image types to identify these tumors in the lung. However, the random location and shape of the tumors and poor quality of CT scans are biggest challenges in lung cancer tumor detection. In this paper, a multi-step method for detecting cancer tumors in CT scans is proposed. In the proposed method, the images are first clustered using the super pixel algorithm. The morphological operators are then used to cut the unconnected parts. Finally, the cancerous nodules and tumors are identified using the active contour algorithm. The performance of the proposed approach is evaluated on benchmark LIDC database in terms of Dice similarity measure which is 84.88%. Results show the higher performance of the proposed approach in comparison with state-of-the-art methods in this area.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".