Comparative Analysis of Deep Learning Models on Brain Tumor Segmentation Datasets: BraTS 2015-2020 Datasets
Bibliographic record
Abstract
Deep Learning neural networks have shown applicability in segmentation of brain tumor images.This research have been carried for comprehensive review of several deep learning neural networks. The datasets included in this study are standard datasets Multimodal Brain Tumor Segmentation (BraTS). This paper has summarized the performance of various deep learning neural network algorithms on BraTS datasets. Algorithms have been compared and summarized against the baseline models with specific attributes like dice score, PPV and sensitivity. It has been found that out of the different models applied on the BraTS 2015 dataset GAN in the year 2020 algorithm is showing better results on this data set. GAN architecture termed RescueNet gave the best segmentation results in terms of 0.94 dice score and 0.88 Sensitivity. This has been also observed that models used cascaded deep learning models had independent deep learning models at each stage which had no correlation among the stages which can cause class imbalance. Further it have found that the Attention models tried to solve problem of class imbalance in the brain tumor segmentation task. This work also found that existing CNN’s is having overfitting issues. For this ResNet models can add a rapid connect bounce relationship parallel to the layers of CNN to accomplish better outcomes for the brain tumor segmentation task.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".