The Detection of Brain Tumors Using Chan-Vese Active Contour Without Edges Method in Magnetic Resonance (MR) Images
Bibliographic record
Abstract
Accurate and automatic detections of brain tumors are vital. The aim of this study is to detect brain tumors in Magnetic Resonance (MR) images and to classify these tumors with a high degree of accuracy. After removing skull, the suspicious regions including tumors in the MR images were detected by using K-means clustering, K-means clustering in Lab color space, and the Chan-Vese without edges algorithm. At this stage, a performance evaluation of these three different methods was investigated, and it was seen that the best result was obtained in the Chan-Vese active contour without edges algorithm. For the classification stage, various features such as shape-based features, gray level co-occurrence matrix features, histogram of oriented gradients features, local binary pattern features, and statistical features were extracted from the detected suspicious regions. Finally, the suspicious regions were classified by k-nearest neighbor (k-NN), Fisher’s linear discriminant analysis (FLDA), random forest, decision tree, support vector machines (SVM), logistic linear classifier (LLC), and Naive Bayes classification methods. As a result of this study, it was determined that the FLDA classifier provided the best results with 93.01% accuracy, 93.46% sensitivity, and 96.50% specificity rates in classification for benign tumors, malignant tumors, and healthy (without tumor) cases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".