Automated Detection of Liver Cirrhosis in Medical Imaging Using the InceptionResNetV2 Deep Learning Model for High-Accuracy Classification
Bibliographic record
Abstract
The study examines the use of the InceptionResNetV2 deep learning model to conduct automated detection and staging of liver cirrhosis based on medical imaging data. The use of key clinical variables comprising of age, bilirubin levels, albumin and platelets allowed comprehensive analysis on a robust basis facilitating comprehensive classification of the status of cirrhosis. Confusion matrix, ROC curve, box plots, and histograms were employed to measure the efficiency of the model in separating classes, and how the features are distributed. Even though the model showed nearly satisfactory correspondence with clinical categories in some populations, the ROC curve provided AUC of 0.51, which is very low to discriminate two stages in a binary mode. Remarkably, bilirubin level became more variable and more pronounced in advanced disease groups, which shows that it plays a critical role in liver cirrhosis. The study helps bring into focus the intricacies of utilizing deep learning in attaining superior accuracy in classifying the cirrhosis disorder, pointing also to the necessity of greater optimization and incorporation of more various clinical characteristics to enhance the predictive modeling skills of medical imaging.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".