Classification of Brain Tumors from MR Images Using a New CNN Architecture
Bibliographic record
Abstract
Accurately classifying brain tumors is a crucial factor in combatting, intervening, and treating the disease.By automating the tumor diagnosis process without the involvement of human factors, it is possible to decrease the occurrence of human errors during the diagnosis process.In a new deep convolutional neural network architecture was developed to tackle the brain tumor classification problem, resulting in the successful classification of three distinct types of brain tumors -meningioma, glioma and pituitary.With the propose CNN architecture, a classification accuracy of 98.69% was achieved in brain tumor classification.The recommend model is simple and very fast.It was observed that giving high kernel size and strides values in the first layers and low values in the middle layers of the convolutional layers, and keeping the strides value small in the pooling layer had greatly increased on the model performance.The recommend CNN architecture was compared with studies using the same dataset and transfer learning models in the literature.As a result of these comparisons, high-scoring results were obtained with the recommend model.The classification success achieved by the model is state-of-the-art among stand-alone models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".