Bibliographic record
Abstract
Malignant Neoplasm commonly referred as cancer is caused by uncontrolled growth of cells in the body. According to the American Cancer Society nearly 7.6 million people died from cancer during 2007. The true inspiration for this paper comes from the paper "Implementing automated diagnostic systems for breast cancer detection" by E. D. Ubeyli, achieved appealing results by using different kinds of Neural Network algorithms such as Combine Neural Network(CNN) Recurrence Neural Network(RNN), Probabilistic Neural Network(PNN), Multilayer Perceptron (MLP) and Support Vector Machine (SVM)). We used a hybrid approach for the same diagnosis. The hybrid system that we used was Neuro-Fuzzy (ANFIS-MATLAB) which is a combination of Neural Network and Fuzzy Logic. As an extension of this research and curiosity to evaluate the hybrid approach we implemented a Fuzzy Inference System(FIS) in MATLAB using fuzzy toolbox. The hybrid system trained on equally distributed dataset outperforms all other approaches discussed in literature. Specifically the sensitivity obtained in our Neuro-Fuzzy system is 100% which outperforms sensitivity of 99.37% in the SVM (Support Vector Machine) model used by E. D. Ubeyli [5].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".