Reliable Diagnosis and Prognosis of Gastric Cancer Using Comparative CNN and VGG Deep Learning Models on Multimodal Imaging
Bibliographic record
Abstract
Timely and accurate detection of gastric cancer is key to better patient outcomes, and deep learning algorithms like CNNs and VGGs have demonstrated potential in the multiclass prediction of multimodal imaging. The multimodal imaging data used in this study were trained with CNN and VGG models, and the performance of the model was evaluated based on accuracy, precision, recall, F1 score, ROC curves, and confusion matrices. The CNN model produced better results as it had higher accuracy (0.70) and precision (0.80) than VGG (0.40) and a better recall and F1 score 0.70, 0.67, and good class discrimination shown by AUC up to 0.91. The analysis of the confusion matrices of CNN and VGG allowed establishing that CNN was reliable in identifying the stages of the disease, especially when it perfectly predicted stage2 cases, but VGG demonstrated a lack of class balance and reliability. The results indicate that CNN is more able to diagnose and prognose gastric cancer compared to VGG, highlighting the large clinical potential of deep learning methods on imaging to achieve strong patient stratification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".