ARIMA model based breast cancer detection and classification through image processing

Computer Aided Diagnosis (CAD) has changed the way of medical diagnostics. As similar to other walk of diagnostics field, CAD is having high potential in breast cancer prognosis because of its highest accuracy. CAD may play a very important role in developing countries i.e. EIT-MEM (Electrical Impedance Tomography - Multi-frequency Electrical Impedance Mammography) device being used for breast cancer defection. MEM-EIT produces tomography based mammograms which are considered most reliable method of early detection of breast cancer. Cancer diagnostic expert all over the world find this noninvasive technique very accurate as it is one dimensional representation of images in terms of temperature however the accuracy is limited and investigator fail to take into account the spatial co-ordination between the pixels which is crucial in cancerous tumour detection and their classification (cancerous or normal) in EIT (Electrical Impedance Tomography) - based mammogram images. In this study, we are trying to focus an algorithms based CAD (Computer Aided Diagnosis) model for tumour detection and classification. We model it by ARIMA model (autoregressive integrated moving average (ARIMA) model) and parameter estimation will be performed using leas-square method. Our system classifies the tumour into three categories - (i) healthy tissue (ii) benign tissue (iii) cancerous tissue along with above three segments the performance analysis between 2D image and 1D image will be done for better accuracy and sensitivity detection.