Enhancing Brain Tumor MRI Classification with CNNs Using Transfer Learning
Bibliographic record
Abstract
Brain tumor classification from MRI scans poses significant challenges due to the complex and time-intensive nature of the task. In the advancement of deep learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), has substantially advanced automated image processing and diagnostic tools, offering promising solutions for brain tumor classification. In this article, we propose a robust approach for brain tumor identification using a combination of custom and pre-trained CNN architectures, including ConvNeXtBase, DenseNet121, EfficientNetB0, InceptionV3, MobileNetV2, ResNet18, and ResNet50. In order to achieve effectiveness of the proposed method, we combine multiple publicly available MRI datasets by employing preprocessing techniques such as resizing, normalization, and augmentation to enhance the generalization and mitigate over-fitting. The ResNet18 yield the accuracy of 99%. The empirical results demonstrate that the proposed CNN framework, utilizing transfer learning, effectively identifies and classifies glioma, meningioma, pituitary, and no-tumor categories, showcasing impressive generalization capabilities.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".