Unveiling the Hidden: Leveraging Medical Imaging Data for Enhanced Brain Tumor Detection Using CNN Architectures
Bibliographic record
Abstract
Brain tumor detection using deep learning has made significant progress, but there are still several challenges and problems that researchers and practitioners are actively addressing like limited data availability, imbalanced data, generalization of data, data preprocessing and integration into clinical practice.To overcome these challenges, this study proposes the use of several transfer learning techniques and CNNs to provide a unique method for classifying brain tumors.Specifically, we employed three well-known transfer learning architectures, namely VGG, ResNet, and MobileNet, to explore their performance in brain tumor detection.Advantages of using VGG, ResNet, and MobileNet models include their ability to leverage pre-trained knowledge, adaptability to different problem domains, architectural diversity, simplicity, efficiency, state-of-the-art performance.Deep learning and models with prior training are used to improve the accuracy and efficiency of classifying brain tumors.The comparative study of various models showed that in order to classify brain tumor images, MobileNet stands out with the highest accuracy of 98.66% as compared to 97.55% of VGG and 87.44% of ResNet.The outcomes of this project help advance the field of diagnostic imaging and aid medical practitioners in the prompt and precise identification of brain tumors.
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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) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".