An Artificial System for Prognosis Cancer Cells through Blood Cells Images Using Image Processing

In present scenario, imaging in act an important role throughout the integrated medical process from indicative and find out about diseases through studies. Considering most of the imaging techniques have gone directly digital, with unceasingly increasing perseverance, these medical image processing has to confront many upcoming challenges from broad data measures. In our paper we describe the process of analysing cancer cell and how image processing is helpful and immensely important in medical science. The paper analyse and discover bacteria under blood cells through its rate of growth of bacteria in blood with the help of object recognition technique of image processing by getting the image through microscope. This work presents an precise way of static image processing technique of object recognition for detection and prognosis of cancer cell. This method used for diagnosis of abnormal growth of cells in any body part using blood cell image. This procedure include artificial expert system techniques, such as machine learning, artificial neural network, and fuzzy logic with medical imaging techniques. We subsist blood images as input for our expert system to analyse and detect the classification of growth of abnormal cell through static image processing technique. This paper presents another technique of image processing as pattern recognition for analysis and classification of cancer cells using microscopic blood cells images. . The proposed method achieved sensitivity of 80 %. specificity of 91.04 % and accuracy of 96.4 %.