Convoluted neural network and transfer learning algorithm for improved brain tumor classifications in MRI
Bibliographic record
Abstract
Artificial intelligence (AI) has made significant use cases to improve patient care, particularly in medical image analysis. This study aims to develop a deep-learning model for disease classification in medical images and compare its performance in four-class MRI and two-class X-ray classification tasks. We utilize Convolutional Neural Networks (CNNs) for diagnosing pneumonia from chest X-rays and various tumors from brain MRIs, leveraging transfer learning to improve performance. Transfer learning, which reuses pre-trained models like VGG-16, is more efficient than building models from scratch. The VGG-16 model, pre-trained on over a million ImageNet images, achieved 92.7% accuracy. By fine-tuning, we reached 93.6% accuracy. Data augmentation techniques, such as flipping, rotation, and brightness adjustments, further enhance classification accuracy and performance.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".