A Deep Learning Approach for Brain Tumor Diagnosis: Combining an 8 Layer CNN with Rigorous K-Fold Validation
Bibliographic record
Abstract
A brain tumor is an abnormal growth of brain cells that may manifest symptoms of cancer.Early and accurate detection is essential to initiate timely treatment and improve patient outcomes.Traditional diagnostic methods often demonstrate limited accuracy, highlighting the need for more reliable and automated solutions.This study proposes an optimized 8-layer convolutional neural network (CNN) for automatic brain tumor classification using magnetic resonance imaging (MRI) scans.A balanced dataset of 3,000 annotated MRI images was used (1,500 with tumors and 1,500 without tumors).Preprocessing included image labeling.To improve training efficiency, preprocessing procedures included image labeling, resizing, and augmentation.Model Performance was evaluated with five-fold cross-validation with an 80-20 train-test split.The proposed CNN achieved an accuracy of 97%, outperforming established deep learning models such as ResNet50 (72%), VGG16 (94%), MobileNetV2 (94%), and VGG19 (92%) on the same dataset.These findings show that the proposed lightweight CNN provides high diagnostic accuracy with reduced computational complexity.Hence, this approach exhibits strong potential for integration into clinical diagnostic workflows, supporting more efficient and accurate brain tumor detection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".