Data Mining Techniques In Computer Aided Cancer Diagnosis

1. Data mining techniques in computer aided cancer diagnosis

2. Data mining in cancer diagnosis Bio medical data mining happens to be a long standing problem in scientific research where scientists are looking for innovative ideas methods to mine bio-medical data. Data mining in CAD(computer aided diagnosis) helps doctors to make optimal decisions quickly and accurately.

3. Data mining techniques allow the doctors to quickly categorize the difference between malignant and benign tumors. Key factor analysis is done to find the difference between benign and tumor cells. Decision trees may be used for clustering, classification, prediction or estimation. One of the useful applications in medical sciences is in management of leukemia as it accounts for about 33% of pediatric malignancies.

4. ALL Child acute lymphatic leukemia( also called acute lymphatic leukemia or ALL) is a cancer of blood and bone marrow. Its most common in children and gets worse if not treated in early stages.

5. Key data mining techniques used in ALL diagnosis are: Neural networks Decision trees Cluster detection Genetic algorithms

7. Combine information from multiple sources

8. Provide support for differential diagnosis

11. Detect cancer patterns in general and special populations

12. Formulate models that explains the patterns

14. New image mining techniques for breast cancer detection The method uses an overlapped technique to precisely detect the presence of breast cancer. Statistical features are extracted from mammograms and used for decision tree induction in order to learn the knowledge for computer assisted image analysis.

15. The application of data mining will help to get some additional knowledge about specific features of different classes and the in which they are expressed in the image (can help to find some inherent non-evident links between classes and their imaging in the picture) It can help to get some non-trivial conclusions and predictions can be made on the base of image analysis. The basis for this study is sufficiently a large database with images and expert descriptions.

17. A data viewer that provides a series of reports.

18. A map viewer that allows users to project compounds onto color maps for rapid visual analysis.

19. Links to external publications databases for related articles of interests.