Abstract

Dr Sudhir Kumar Meesala, Sonia Wadhwa*

ABSTRACT

Article History Received: July 2019 Accepted: August 2019 Cancer detecting processor was synthesized by an image processing method with tensor flow open-source software. Microscopic images of blood samples were characterized by image processing technique; tensor flows open source software and training process of image sample. Our previous paper “an artificial system for prognosis cancer cells through blood cells images” is based on the analysis of blood cell images. The image processing method revealed that it was an old methodology in medical science. The method used for diagnosis of abnormal growth of cells in anybody using blood cell image sample. It is found that in urban areas, people can go through a diagnosis of cancer easily they have some knowledge about medical science but in rural areas, people are lack of knowledge about the diagnosis as well as they are not financially capable of it. This paper reveals how this cancer detection processor is helpful for rural areas. This procedure involves artificial expert system techniques, like machine learning, artificial neural network, with imaging techniques. Calculations say that this processor is a prototype and it contains sensitivity of 80%. The specificity of 91.04% and accuracy of 96.4%. On the basis of the above result, the detector responds that whether the blood sample contains cancer cell or it is normal blood cell. For this detection, we have already described the stepwise algorithm and flow chart to understand the methodology of this system. This paper tells how this system is helpful in today’s scenario.