Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks
Bibliographic record
Abstract
Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in delayed diagnosis and treatment. In response, Computer-Aided Diagnostic (CAD) tools can be used to potentially streamline and expedite the diagnostic process. Recently, deep learning techniques have gained prominence in the automated analysis of CXR images, particularly in segmenting lung regions as a critical preliminary step. This study aims to develop and evaluate a lung segmentation model based on a modified U-Net architecture. The architecture leverages techniques such as transfer learning with DenseNet201 as a feature extractor alongside dilated convolutions and residual blocks. An ablation study was conducted to evaluate these architectural components, along with additional elements like augmented data, alternative backbones, and attention mechanisms. Numerous and extensive experiments were performed on two publicly available datasets, the Montgomery County (MC) and Shenzhen Hospital (SH) datasets, to validate the efficacy of these techniques on segmentation performance. Outperforming other state-of-the-art methods on the MC dataset, the proposed model achieved a Jaccard Index (IoU) of 97.77 and a Dice Similarity Coefficient (DSC) of 98.87. These results represent a significant improvement over the baseline U-Net, with gains of 3.37% and 1.75% in IoU and DSC, respectively. These findings highlight the importance of architectural enhancements in deep learning-based lung segmentation models, contributing to more efficient, accurate, and reliable CAD systems for lung disease assessment.
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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 | 0.000 |
| 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) | 0.002 | 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".