OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN
Bibliographic record
Abstract
The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-precision nodule detection method designed for detecting small nodules, particularly in computed tomography (CT) images. This paper presents an improved Faster R-CNN by introducing an optimized multi-scale convolutional neural network (OMS-CNN) technique for feature map generation. This approach aims to achieve an optimal feature map through metaheuristic optimization by combining the last three layers of the VGG16 architecture. The advanced parameter-setting-free harmony search (PSF-HS) algorithm is utilized to implement this method, automatically adjusting the number of channels in the composite layers as a hyperparameter. The beetle antenna search (BAS) optimization algorithm is utilized to effectively initialize the kernel filter weights and biases in the composite layers, thereby enhancing training speed and detection accuracy. In the false-positive reduction stage, a combination of multiple 3D deep convolutional neural networks (3D DCNN) is designed to reduce false-positive nodules. The proposed model was evaluated using the LUNA16 and PN9 datasets. The results demonstrate that the OMS-CNN technique effectively extracted representative features of nodules at various sizes, achieving a sensitivity of 94.89% and a CPM score of 0.892. The comprehensive experiments illustrate that the proposed method can enhance detection sensitivity and manage the number of false positive nodules, thereby offering clinical utility and serving as a valuable point of reference.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".