Enhancing Liver Tumour Segmentation in CT Images Using Dilated Residual Capsule Networks
Bibliographic record
Abstract
Liver CT images play a crucial role in the early diagnosis of liver disorders and have proven effective in identifying chronic liver disease, which may lead to fatal outcomes.This imaging technique provides detailed cross-sectional views, allowing for precise detection of abnormalities, aiding in timely intervention, and improving patient prognosis.The detection process of chronic liver disease should be carried out with meticulous accuracy.Due to the inherent complexities involved and the presence of ambiguities in CT images, segmentation approaches have not yet reached the pinnacle of accurate and reliable performance required for clinical application.Recently, the emergence of machine learning and deep learning algorithms has provided valuable insights into achieving a more accurate segmentation process.However, these existing deep learning algorithms suffer from several challenges that hinder segmentation performance.Hence, independent deep learning algorithms require further refinement to handle CT liver images effectively.To address this problem, this research article proposes a fully automated, robust, and accurate segmentation of CT liver images based on a deep neural network architecture that adopts dilated residual networks integrated with powerful capsule networks.This proposed network combines the strengths of capsule networks and ResNet-50 architectures to achieve better segmentation results.Extensive experimentation is conducted using 100 healthy subjects, and 131 contrast-enhanced image data are used for training, while 70 CT images are used for testing.Furthermore, the proposed model is evaluated using performance metrics such as DICE, Intersection over Union (IoU), precision, and recall.To demonstrate the superiority of the suggested network, its segmentation performance is compared with that of existing stateof-the-art deep learning architectures.The results demonstrate that the suggested model achieved 0.98 DICE, 0.95 IoU, 99.2% precision, and 99.1% recall, respectively, surpassing various existing models used for liver CT image segmentation.
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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 | 0.002 |
| 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".