Detection of Tumor in Liver Using Image Segmentation and Registration Technique
Bibliographic record
Abstract
Now-a-days, there is rise in the death rate of patients suffering from liver cancer.The liver cancer rate is increasing year by year.Generally, liver cancer's death rate is very high because the disease causes no symptoms, so it's often not caught until it's in final stages.The Canadian Cancer Society says, if we catch someone's liver cancer early, their chance of defeating the disease is 70 to 80 per cent.If the disease is caught late, the average person survives about a year after diagnosis.We propose an algorithm for liver cancer detection which is based on concepts of fuzzy logic and neural network.Neuro-fuzzy (NF) systems are suitable tools to deal with uncertainty found in the process of extracting useful information from images.In this work, the liver tumor is detected through the medical images in three phases, pre-processing phase, processing phase and detection phase.Initially in the pre-processing phase, a set of medical images is filtered for removing noise.Then the filtered image is segmented automatically using fuzzy logic, neural network and windowing technique.In the detection phase neuro-fuzzified segmented images of CT and MRI is registered to obtain the tumor.The result is obtained for few different set of database.
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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) | 0.000 | 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 | 0.000 | 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".