Summary of Effect Sizes As a Statistical Feature-selector-based Learning to Detect Breast Cancer, by Nicolas Masino et al.
Effect sizes as a statistical feature-selector-based learning to detect breast cancer
by Nicolas Masino, Antonio Quintero-Rincon
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers aim to improve breast cancer detection by developing a machine learning algorithm that reduces data dimensionality using statistical features extracted from cell nuclei images. The proposed approach combines feature selection with parametric effect size measures to select relevant features and improve model performance. Experimental results show an accuracy of over 90% when using a support vector machine (SVM) classifier with a linear kernel, demonstrating the feasibility of this method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists are working on ways to detect breast cancer more accurately. A new approach uses math to pick important details from images and make decisions about whether someone has breast cancer or not. This helps doctors find cancer earlier and treat it better. The idea is simple: use math to look at pictures of cells and pick out the most important parts. Then, use those parts to decide if there’s cancer or not. This new way works really well – it was right over 90% of the time! |
Keywords
» Artificial intelligence » Feature selection » Machine learning » Support vector machine
