Enhanced Brain and Lung Tumor Detection by Explainable AI Techniques
Bibliographic record
Abstract
Cancer accounts for most deaths worldwide, and cases of brain and lung tumors are emerging at a rapid pace.Early detection is of prime importance for better patient outcomes, but the conventional methods of diagnosing cancer rely upon MRI scans, and they are timeconsuming, two-dimensional, and a potential source of inaccuracies.In India alone, more than 70,000 cases are reported of lung cancer.About 50,000 individuals have brain tumors.This research uses deep learning models-sequential model and the pre-trained VGG-16 model-to provide accurate classification for brain and lung tumors from MRI and CT images.With a combination of machine learning and image processing, the automated system reduces false negatives and false positives, thereby attaining high accuracy in diagnosis.Additionally, the use of Explainable AI (XAI) techniques improves the interpretation of predictions by healthcare professionals.These advanced, automated solutions are thus directed toward enhanced early cancer detection in the pursuit of better patient outcomes.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".