Automated Identification and Classification of Brain Tumors Using Hybrid Machine Learning Models and MRI Imaging
Bibliographic record
Abstract
The need for automated diagnostic systems in medical imaging, particularly in the detection and categorization of brain tumors, is paramount.This research proposes a hybrid model to identify and classify MRI-detected brain tumors into four categories: pituitary, meningioma, glioma, or absence of a tumor.This hybrid approach leverages the strengths of both deep learning and traditional machine learning techniques, enabling the extraction of complex features and the recognition of intricate patterns, such as those found in brain tumors.Machine learning further enhances the model's capacity to classify accurately based on these specific features, reducing time and cost.The proposed system consists of several stages: initial pre-processing of brain MRI images, the application of two distinct segmentation techniques (region-based and edge-based), morphological operations, feature extraction, and finally classification.The classification employs a hybrid model (VGG16) in conjunction with four traditional classifiers: Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF).The experimental results highlight that the use of Random Forest with region-based segmentation yields the highest accuracy, reaching 99.17%.This combination excels at focusing on minute yet crucial details in MRI images and maintains stability in the presence of distortion and outliers.The dataset employed in this study is an amalgamation of three: Figshare, SARTAJ, and Br35H, each containing MRI images of the aforementioned four types of brain tumors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".