Performance Evaluation of Feature Extraction and SVM for Brain Tumor Detection Using MRI Images
Bibliographic record
Abstract
The aim of this study is to develop an automatic detection of brain tumors from magnetic resonance images based on artificial intelligence.The developed approach comprises three steps: pre-processing, feature extraction, and classification.The pre-processing consists of applying image processing techniques to improve contrast and reduce noise in magnetic resonance images.The feature extraction consists of transforming magnetic resonance images into numerical vectors that represent the discriminating attributes for tumor detection.Then the classification consists of using a machine-learning algorithm to separate magnetic resonance images into two classes: tumoral and non-tumoral.The performance evaluation of the proposed approach is tested under dataset of 3000 magnetic resonance images, where 1500 magnetic resonance images are with tumors and 1500 magnetic resonance images are without tumors.In the feature extraction step, two techniques have been used the bag of features and the ResNet50 convolution neural network then a comparison between them was performed.In the last step, the obtained images have been compared with the three different kernels function for the support vector machine classifier: Linear, Quadratic, and Cubic.The proposed magnetic resonance images classification approach was tested using confusion matrices and receiver operating characteristic curves, which revealed satisfactory performance in terms of Sensitivity, Precision, Specificity and Accuracy.The obtained results show that the BoF-SVMs combination achieves the best classification accuracy, with a recognition rate of 100%.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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 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".