Transformative Impact of AI on Early Diagnosis and Treatment of Lung Cancer with a Decade of Advances in Medical Imaging and Prognosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer is the second leading cause of mortality worldwide, largely due to low survival rates resulting from diagnosis at advanced stages. This paper focuses on how machine learning (ML) and deep learning (DL) algorithms have evolved over the past decade to improve cancer detection and classification, emphasizing the importance of early diagnosis. Convolutional Neural Networks (CNNs) have demonstrated an accuracy of 89.5% in medical image recognition, highlighting their effectiveness in imaging-based diagnosis. Recent advancements such as YOLOv7 further outperform traditional diagnostic methods by providing more accurate tumor detection. Prognostic analysis using Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks has achieved accuracies of 82.3% and 84.7%, respectively. Ensemble methods exhibit superior performance with an impressive accuracy of 91.2%, outperforming individual models. Additionally, data augmentation using Generative Adversarial Networks (GANs) improves precision to 76.8%, underscoring the importance of synthetic data generation in addressing data scarcity. These findings collectively demonstrate the transformative impact of artificial intelligence in oncology and emphasize the significance of integrated, collaborative approaches for achieving improved cancer diagnosis and treatment outcomes.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it