Optimized H-StrokeNet: A Deep Learning Network for Stroke Classification
Bibliographic record
Abstract
Hybrid classifiers are neural network models that are structured with two different network architectures to create the best deep learning model.The hybrid models are developed generally to meet certain complex feature learning problems.However, the nature of hybrid classifiers causes the network nodes to learn 'n' number of features, which increases the computational complexity of the classifiers.Therefore, the implementation of a hybrid classifier reaches an impossible state when the processor and memory spaces are restricted in real-time applications.The proposed work utilizes the feedforward neural network architecture with two hidden layers in combination with the eXtreme Gradient Boosting (XGBoost) algorithm to form a hybrid classifier called H-StrokeNet, and a Crow Search Optimization (CSO) algorithm is included in the work to reduce the complexity of the hybrid network by providing optimized feature extraction.The performance of the proposed work is verified with the regular XGBoost algorithm and deep neural networks using the Laboratory of Processing Image, Signals, and Computer Science (LAPISCO) dataset, and a comparative analysis is made with different algorithms such as support vector machine (SVM), Decision Tree (DT), Na ve Bayes (NB), k-Nearest Neighbor (kNN), and XGBoost.Results show the superior performance of the proposed system with an overall accuracy of 96.55%, whereas it is 92.09% with the XGBoost classifier.
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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 | 0.000 | 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".