MRI Brain Tumor Identification and Classification Using Deep Learning Techniques
Bibliographic record
Abstract
Deep learning has exponentially enhanced the state-of-the-art in several Artificial Intelligence (AI) domains, including computer vision, user authentication, healthcare, object recognition and image processing.Recently, deep rule-based classifier (DRB) is being employed to solve diverse problems of classification or prediction.Thus, in this paper, we present a novel, simple, automatic, and effective DRB classifier-based scheme for MRI brain tumor classification.The proposed framework is composed of three stages, i.e., preprocessing, feature extraction and classification.Especially, in the second stage, we have investigated and analyzed comparative performances of various deep features extracted by the AlexNet, VGG-16, ResNet-50, ResNet-18 deep learning networks.After feature extraction step, a DRB classifier is employed for classification.The proposed method is evaluated on two publicly available datasets that are available on Kaggel website.The first database is a binary database (i.e., tumor and no tumor).Whereas the second one is a multiclass database (i.e., Meningioma, Glioma and Pituitary tumor).Experimental results show that the proposed method can obtain notable performances.Moreover, the comparative study with classical methods (e.g., SVM, KNN, Decision tree) as well as several state-of-the-art distance techniques demonstrated the effectiveness of proposed approach in MRI brain tumor detection and classification.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".