Design of an Iterative Cluster-Based Model for Detection of Brain Tumors Using Deep Transfer Learning Models
Bibliographic record
Abstract
A tumor develops when brain cells exhibit abnormal growth patterns within various body locations, characterized by irregular boundaries and shapes.Typically, these tumors exhibit rapid proliferation, increasing at a rate of approximately 1.6% per day.This abnormal cell growth can lead to invisible illnesses and alterations in psychological and behavioral functions, contributing to a rising trend in adult mortality rates worldwide.Therefore, Brain tumors must be detected early.Failure to do so may cause a deadly, incurable condition.Effective brain tumor therapy improves survival if detected early.Magnetic Resonance Imaging (MRI) is essential for finding and classifying brain tumors.The manual nature of brain tumor diagnosis and classification makes it prone to errors, necessitating the development of automated processes for improved accuracy.In light of these considerations, we have devised with a fully automated way to use MR images to find and classify brain tumors.Our approach encompasses three key phases: pre-processing, segmentation, and classification.To detect tumors in the brain, we utilized MRI, employing the deep transfer with the transformed VGG19 model.Notably, our research demonstrates superior growth rates when using other pre-trained Convolutional Neural Network (CNN) models such as AlexNet and VGG-16.The deep transfer learning with the transformed VGG19 model yielded accuracy achieving 98.65% (dataset 1) and 99.18% (dataset 2) for different datasets.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".