Deep Learning Based Brain Tumor Classification, Localization and Severity Assessments Using CNNs
Bibliographic record
Abstract
In the rapidly evolving field of medical imaging and technology, accurate diagnosis, classification, localization, and severity assessment of brain tumors is vital for creating effective treatment plans and making accurate predictions about the disease. To automate these important evaluations, we introduce a deep learning system that leverages Convolutional Neural Networks (CNNs) and ResUNet. This technology is designed to classify various brain tumors, locate them accurately, and determine their severity using MRI scans. The CNNs are effective in handling all the classification tasks as they are highly capable of extracting and learning complex features from images. For accurate segmentation the ResUNet architecture is employed, enhancing segmentation accuracy through residual learning. The segmented regions are then analyzed to determine the severity of tumor based on size, shape, and spread. This integrated approach highlights the potential of deep learning to advance brain tumor diagnosis and treatment, thereby promoting personalized and effective healthcare solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".