Breast cancer detection, classification, scoring and grading of histopathological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. In a large hospital, a pathologist typically handles number of cancer detection cases per day. It is, therefore, a very difficult and time-consuming task. This paper proposes a method for automatic Breast cancer detection, classification, scoring and grading to assist pathologists by providing second opinions and reducing their workload.

A computer-aided Breast cancer detection, classification, scoring and grading system for tissue cell nuclei in histological image is introduced and validated as part of the Biopsy Analysis System. Cancer cell nuclei are selectively stained with monoclonal antibodies, such as the ant estrogen receptor antibodies, which are widely used as part of assessing patient prognosis in breast cancer. This paper also presents the classification of micro cancer object of breast tumor based on feed forward back propagation Neural Network (FNN).

Twenty six hundred sets of cell nuclei characteristics obtained by applying image analysis techniques to microscopic slides. The dataset consist of eight features which represent the input layer to the FNN. The FNN will classify the input features into type4, type3, type2 and type1 cancer affected objects. The sensitivity, specificity and accuracy were found to be equal 99.10%, 95.70% and 98.60% respectively. It can be concluded that FNN gives fast and accurate classification and it works as promising tool for classification of breast cell nuclei.