Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning
Bibliographic record
Abstract
The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%.
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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 | 0.001 | 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".