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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".