Pancreatic Tumor Detection by Convolutional Neural Networks
Bibliographic record
Abstract
Artificial Intelligence and its sub-branches like Machine Learning (ML) and Deep Learning (DL) applications have the potential to have positive effects that can directly affect human life. Medical imaging provides a way for the internal structure of the human body to be visible with various methods. With DL models, cancer detection, which is one of the most lethal diseases in the world, from medical images can be made possible with high accuracy. The main objective of this paper is to detect Pancreatic Cancer, which is one of the cancer types with the highest fatality rate, from a dataset of Computed Tomography (CT) images, which is one of the medical imaging techniques and has an effective structure in Pancreatic Cancer imaging. The designed DL model is integrated into the Flask application to develop a web application. With this application, early diagnosis of pancreatic cancer can be achieved, which progresses insidiously and therefore does not spread to neighboring tissues and organs when the treatment process is started. Due to the abundance of medical images reviewed by medical professionals, this application can assist radiologists and other specialists in Pancreatic Tumor detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".