An Effective Multi-Architecture Approach for Lung Cancer Detection
Bibliographic record
Abstract
Most of the traditional and existing methods in lung cancer detection suffer from challenges such as limited interpretability, delayed decision-making, limited diagnostic assistance, scope of misinterpretations, and single modality processing models.To address these, a hybrid deep learning model is required that consists of a 3 Dimensional Convolutional Neural Network (3D-CNN), a Transformer, and an RNN-LSTM pipeline for the identification of lung cancer.The hybrid model predicts the disease early and alerts so that the mortality rate is reduced.In these, 3D-CNN is used for volumetric CT images and nodules malignant processing, Transformer is used for processing genomics sequences, and the RNN-LSTM pipeline is used for temporal clinical data interpretation.The features from each section are fused using a multi-modal fusion layer for efficient lung disease classification.The results obtained over the LIDC-IDRI dataset (publicly available repository) of images, clinical/genomics data after preprocessing, and hybrid model processing, in terms of AUC, sensitivity, and specificity, are observed to be better than existing models used.The statistical test via DeLong would determine the effectiveness of the model.The interpretability is increased due to the usage of SHAP explainability by clinical and nodules features.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".