Medical diagnosis using artificial neural networks
Bibliographic record
Abstract
Medical diagnosis using Artificial Neural Networks (ANN) and computer-aided diagnosis with deep learning is currently a very active research area in medical science. In recent years, for medical diagnosis, neural network models are broadly considered since they are ideal for recognizing different kinds of diseases including autism, cancer, tumor lung infection, etc. It is evident that early diagnosis of any disease is vital for successful treatment and improved survival rates. In this research, five neural networks, Multilayer neural network (MLNN), Probabilistic neural network (PNN), Learning vector quantization neural network (LVQNN), Generalized regression neural network (GRNN), and Radial basis function neural network (RBFNN) have been explored. These networks are applied to several benchmarking data collected from the University of California Irvine (UCI) Machine Learning Repository. Results from numerical experiments indicate that each network excels at recognizing specific physical issues. In the majority of cases, both the Learning Vector Quantization Neural Network and the Probabilistic Neural Network demonstrate superior performance compared to the other networks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".