Prostate Cancer Detection Using Deep Learning with Clinical-Structural Data Integration in an EfficientNetV2B3 Framework
Bibliographic record
Abstract
Prostate cancer continues to be a cause of cancer-related deaths among men and the creation of better diagnostic instruments is needed in this case. This paper lays out a deep learning model called EfficientNetV2B3 architecture to fuse clinical and structural patient data and improve the prediction of prostate cancer. The degree of training and validation occurred in a labeled dataset by placing clinical variables and structural imaging features into the model. Confusion matrix analysis, receiver operating characteristic (ROC) curve and loss/accuracy tracking of 50 epoch were used to measure the performance of the model. The outcomes indicated that the overall ROC AUC was 0.92 with great discriminatory capacity between malignant and benign. The confusion matrix showed accurate prediction of 8 benign and 4 malignant cases with 8 malignant cases being predicted as benign and 0 benign cases as malignant. During the course of training validation accuracy was always above 70 percent and reached well above 90 percent during training. The results make EfficientNetV2B3 promising as deep learning to use multimodal data in prostate cancer diagnostics.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".