Accuracy in breast cancer detection in various models

 Early-stage breast cancer detection has seen significant advancements, particularly with the integration of machine learning and deep learning techniques. Here are some key points:


1. **Machine Learning Models**: Various machine learning algorithms have been developed to predict breast cancer at an early stage. Some models have achieved accuracy rates as high as 97%³.


2. **Deep Learning Approaches**: Deep learning models, especially those using digital mammography and histopathological images, have shown promise. For instance, a study using deep learning to analyze histopathological images reported a cross-validation accuracy of 62.4% for predicting early recurrence².


3. **Comprehensive Reviews**: Systematic reviews of AI applications in breast cancer risk prediction highlight the potential of these technologies to improve early detection and personalized risk management¹.


4. **Adaptive Boosting (AdaBoost)**: In controlled settings, the AdaBoost classifier has demonstrated an accuracy of 98.23% for early breast cancer detection⁵.


These advancements are promising for improving early detection and treatment outcomes. Are you interested in any specific aspect of these technologies or their applications?


Source: Conversation with Copilot, 30/7/2024

(1) Early-Stage Prediction of Breast Cancer Using Suggested Machine .... https://link.springer.com/chapter/10.1007/978-981-99-3485-0_43.

(2) Predicting early breast cancer recurrence from ... - Nature. https://www.nature.com/articles/s41523-023-00597-0.pdf.

(3) Frontiers | Breast cancer risk prediction using machine learning: a .... https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1343627/full.

(4) Machine learning-based models for the prediction of breast cancer .... https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02377-z.

(5) Breast Cancer Prediction: A Comparative Study Using Machine ... - Springer. https://link.springer.com/article/10.1007/s42979-020-00305-w.

(6) https://doi.org/10.3389/fonc.2024.1343627.

(7) https://doi.org/10.1038/s41523-023-00597-0.

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