Mining Breast Cancer Classification Rules from Mammograms
| Autor: | Jinn-Yi Yeh, Si-Wa Chan, Tai-Hsi Wu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Oncology
medicine.medical_specialty computer-aided diagnosis system Computer science Science 02 engineering and technology mammograms QA75.5-76.95 01 natural sciences 010104 statistics & probability Artificial Intelligence Internal medicine Electronic computers. Computer science 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing breast cancer classification 0101 mathematics Breast cancer classification Software Information Systems |
| Zdroj: | Journal of Intelligent Systems, Vol 25, Iss 1, Pp 19-36 (2016) |
| ISSN: | 0334-1860 |
| Popis: | Breast cancer is a leading cause of cancer death in women. Early diagnosis and treatment are crucial to reduce the mortality rate and increase patients’ lifespan. Mammography is effective in early detection. This study proposes a computer-aided diagnosis system based on the mini-Mammographic Image Analysis Society database for analyzing mammograms. After selecting the regions of interest, we computed three typical features: the shape, spatial, and spectral domain features. We then applied the structural equation model to obtain relations between the features and the breast tissue type, lesion class, and tumor severity after feature extraction by information gain. Finally, we used the decision tree and classification and regression tree to construct computer-aided diagnosis rules; we generated 10 rules for predicting the classification of abnormal lesions and 11 rules for classifying the tumor severity. These rules can help clinicians detect and identify breast cancer efficiency from mammograms and improve medical care quality. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|