Automated Brain Tumor Detection with Advanced Machine Learning Techniques
Bibliographic record
Abstract
Early diagnosis is essential for the prognosis of brain tumors. Conventional methods of brain tumor classification involve biopsy through invasive brain surgery. Here we worked on the analysis of 3000 Magnetic Resonance Imaging (MRI) brain images consisting of glioma, meningioma, pituitary tumors and healthy brains to develop non-invasive strategies for the detection of tumors using a machine learning approach. This work included data augmentation to achieve equal numbers of tumor and non-tumor samples 1500 each. Seven methods were used for the classification purpose: Logistic Regression, SVC, KNN, Naïve Bayes, Neural Network, Random Forest, and cluster analysis through K-means. Basic evaluating parameters were used as the performance indicators including accuracy, precision, recall, F1-score, and AUC to determine the efficiency of each model. Out of the four algorithms tested Logistic Regression and Random Forest made the highest test accuracy of 96% they were closely followed by Neural Networks at 95% for tumor versus non-tumor classification. Based on these results, the use of non-invasive MRI-based machine learning as an accurate diagnostic method for tumor detection is highly emphasized, but it requires the enhancement of their diagnostic model to accomplish its high-level goal.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".