Association Rule Mining Based Decision Tree Induction for Efficient Detection of Cancerous Masses in Mammogram

Breast cancer is one of the most common forms of cancer in women. In order to reduce the death rate, early detection of cancerous regions in mammogram images is needed. The existing system is not so accurate and it is time consuming one. The system we propose includes the data mining concept for early, fast and accurate detection of cancerous masses in mammogram images. The system we propose consists of:preprocessing phase, a phase for segmenting normal, benign and malignant regions and a phase for mining the resulted traditional Database and a final phase to organize the resulted association rule based decision tree induction in a classification model. The experimental results show that the method performs well, reaching over 99% accuracy. This is mainly to increase the levels of diagnostic confidence and to provide immediate second opinion for physician.