1.Proposed system is a web based system, which is easily accessible around the world.
2.Proposed system implements an adaptive learning framework in order to diagnose and identify cells of user’s interest efficiently.
3.For example, system can classify and identify the breast cancer tissue types into four categories, including normal, Atypical ductal hyperplasia (ADH), Ductal carcinoma in situ (DCIS) and Breast Carcinoma.
4.For example in 3, system can generate quantitative results of different tissue types and their percentage present.
5.For example in 3, if breast carcinoma is diagnosed by the system, further it can generate prognosis information with the help of predictive immunohistochemical biomarkers (Ki67, Estrogen Receptors (ER), Progesterone Receptors (PR) and Human Epidermal growth factor Receptor-2 (HER2)), including life expectancy, response to a given therapy and may direct patient’s treatment. Moreover, their analysis can become the basis of targeted therapy or personalized medicine.
6.Proposed system trains the new data with the previous model instead of all data in data base. Therefore, our product runs faster.
7.Proposed system has high accuracy, and the result is similar to doctors’ diagnosis.