Se-Resnet: A Novel Method for Gastrointestinal (GI) Diseases Classification from Wireless Capsule Endoscopy (WCE) Images
Bibliographic record
Abstract
The GI tract can develop some medical issues that may require a doctor to evaluate them.These consist of growth anomalies, tissue inflammations, and gastrointestinal issues.In this work, we propose a novel deep-learning (DL) technique to classify the categories of Gastrointestinal Diseases from Wireless Capsule Endoscopy (WCE) images.It has five steps to evaluate.Initially, utilizing the mean filter to remove the noise from given input images.Then extract the features such as shape and position from wireless capsule endoscopy images using the DenseNet-121 technique.To select the features, we utilize the Enhanced Whale Optimization Algorithm (EWOA).Finally, to classify the eight classes of gastrointestinal diseases, we propose a SE-ResNet technique to classify the GI diseases into Ulcerative-colitis, Normal-cecum, Dyed-resection-margins, Esophagitis, Normal-pylorus, Dyed-lifted-polyps, Normal-z-line, Polyps categories with Bald Eagle Search optimization technique to get better accuracy of classification outcomes.In our experiments, we used the Kvasir v2 dataset, and the experiments performed well in terms of recall, precision, accuracy, and f1-score.The performance of the classification technique achieves 99.66% accuracy.The proposed method detects GI disorders on WCE images better than "state-ofthe-art" methods while also classifying the items.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 0.002 |
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".