Development of a Comprehensive Lung Scan Dataset for Machine Learning-Based Lung Cancer Detection
Bibliographic record
Abstract
This study presents the development of a comprehensive lung scan dataset tailored for machine learning-based lung cancer detection, specifically focusing on applying the adaptive Convolutional Neural Network (CNN) technique.Here, this paper proposes Federated Learning-Driven Data Aggregation and Enhancement (FL-DAE) to tackle the issues of data privacy, diversity, and quality when creating an extensive dataset of lung scans for machine learning-based lung cancer detection.The dataset comprises diverse lung scan images from multiple medical institutions, including computed tomography (CT) and X-ray modalities.Rigorous annotation protocols were employed to categorize images into normal and abnormal classes, ensuring accuracy and reliability.Notably, the dataset creation process integrates the Feature-adaptive CNN technique, which adaptively adjusts network parameters based on learned feature representations.This approach enhances the model's ability to capture and leverage discriminative features relevant to lung cancer detection, improving classification performance.Stringent quality control measures were implemented to address artifacts and inconsistencies in the dataset, while ethical considerations were carefully managed to safeguard patient privacy.The resulting dataset, augmented with the Feature-adaptive CNN technique, provides a standardized benchmark for evaluating and advancing machine learning algorithms in lung cancer detection.By leveraging this comprehensive dataset and innovative technique, researchers and practitioners can accelerate the development of more effective and robust approaches for early lung cancer detection, ultimately contributing to improved patient outcomes.The accuracy, false rate, precision and other experimental results of the suggested approach was higher against other established procedures.The designed technique gained high accuracy of 0.99, high precision of 0.98, and high F-Measure of 0.98.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".