Automatic Lung Segmentation in Chest X-Ray Images using Dilated Dense-ResUNet
Bibliographic record
Abstract
Statistical evidence from the World Health Organization (WHO) has shown that lung diseases rank among the primary reasons for fatalities worldwide. With a lack of radiologists interpreting Chest X-Ray (CXR) images for common lung diseases, it becomes a challenge to diagnose lung diseases in a timely manner: Thus, Computer Assisted Diagnostic (CAD) tools can help expedite the procedure. Researchers have begun using deep learning techniques to efficiently analyze lungs in CXR images. To further improve the analysis of lungs, the lung regions can be segmented as a preliminary step. This paper aims to develop and test a lung segmentation model using a modified U-Net architecture. The architecture utilizes transfer learning with DenseNet201, dilated convolutions using a dilation rate of 2 and residual blocks. Numerous experiments were conducted to validate the efficacy of using ImageNet architectures and ImageNet weights on CXR images. Furthermore, different dilation rates were tested to determine the effects of configuring the receptive field. The model was trained using two datasets; Montgomery County (MC) CXR Dataset and Shenzhen Hospital (SH) CXR Dataset. The segmented lungs were evaluated using Jaccard Index (IoU) and Dice Similarity Coefficient (DSC). Compared with results drawn from some of the current state-of-art methods, the proposed model achieves competitive results that has slightly higher IoU and DSC scores.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".