Diagnosis of chest X-Rays images with Deep Learning enhanced with discrete wavelet transform
Bibliographic record
Abstract
Deep learning (DL) is increasingly used in medical imaging to improve image acquisition technology and aid in disease diagnosis and classification. In this work, we present an enhancement of DL applied in lung chest X-rays (CXR) imaging first to enhance lung image segmentation, and second to accurately classify images based on their status of normal, or with tuberculosis, viral pneumonia, or COVID-19 induced pneumonia. The images were obtained from public datasets: 247 from the Japanese Society of Radiological Technology and the Japanese Radiological Society; 138 from the Health and Human Services, Montgomery County (MC), Maryland, USA; 800 from Shenzhen and Montgomery; and 33,920 from Qatar University. For the segmentation, we replaced the Max-Pooling in U-Net++ with the Discrete Wavelet Transform (UNet++-Rand-DWT) integrated with Attention Gates. Prior to U-Net++-Rand-DWT, the images were normalized with the Contrast Limited Adaptive Histogram Equalization (CLAHE) and augmented with the Progressive Growing Generative Adversarial Networks (PGGAN) by generating synthetic images to balance the different image classes. For image classification, the DenseNet-201 model was enhanced with Rand-DWT (DenseNet-201-Rand-DWT). The results for the segmentation obtained with U-Net++-Rand-DWT demonstrated greater accuracy, both qualitatively and quantitatively, compared to U-Net++ with Max-Pooling and to four other models, as evaluated by accuracy, specificity, sensitivity, Dice coefficient and Jaccard Index. For pathology classification, DenseNet-201-Rand-DWT outperformed the traditional U-Net++ and U-Net, and the synthetically generated images with PGGAN significantly enhanced classification accuracy, as determined by precision and F1-score metrics. Furthermore, DenseNet-201-Rand-DWT demonstrated an enhanced performance in discriminating pneumonia and COVID-19 CXR images, even without data augmentation.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".