Feature Extraction for Medical Image Classification: A Novel Statistical Approach
Bibliographic record
Abstract
Medical image classification is an increasingly important area of research, with the need to represent images computationally often posing significant challenges due to the large amounts of data and processing power required.A new approach for image classification in the healthcare domain has been developed in this study, called ASPS_HC, which seeks to obtain higher discrimination among different classes by identifying the most impactful features within the data's Upper and Lower Limit outlier regions.This is achieved through the use of various statistical measures, including the Coefficient of Variance (CV), to create 48 features that represent each image.An experiment was conducted on a dataset of 5,540 diabetic retinopathy images in the Gaussian formula, acquired from Kaggle.The proposed ASPS_HC approach yielded three main advantages over the previous ASPS method for feature extraction: the average rank of the features was increased by 200%, the run time was reduced by 23.30%, and the number of features required was decreased by 50%.As a result, the features extracted using ASPS_HC produced significantly higher accuracy in both the Artificial Neural Network and Random Forest models, with an increase of 1.91% for the former and 1.36% for the latter.
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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 | 0.001 |
| 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) | 0.001 | 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 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".