Auto-Diagnosis of Lung Cancer with the Proposed Feature Fusion-Based Hybrid Deep Model
Bibliographic record
Abstract
Early detection of lung cancer increases the response rate to treatment. Therefore, the accuracy of diagnostic methods is of great importance. Reading the patient's medical images by radiologists can cause a severe time cost besides subjective result. In this context, Artificial Intelligence (AI) methods create an innovative field to reduce the workforce of radiologists and obtain objective results. AI methods play a vital role in improving the analysis of the dataset, extracting meaningful features, clustering, and classification. In our study, the data set contains healthy images besides CT images of malignant and benign tumors with lung cancer; AlexNet is trained using DenseNet 201, GoogleNet, MobileNetV2, and ResNet50 architectures. In addition, a hybrid model has been developed to classify lung CT images. The developed model constitutively used GoogleNet, MobileNetV2, and ResNet50 architectures. The feature maps obtained in these three architectures were combined and classified into different classifiers. Among the classifiers used in the study, the highest accuracy rate was achieved in the Ensemble Subspace KNN classifier. The accuracy value obtained in this classifier is 98.3%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".