Abstract: Automatic advance of leukaemia detection is planned. A physical technique of LEUKAEMIA DETECTION, specialist checks microscopic images. Leukaemia detection is generates in the bone marrow. Lengthy and time taking process which depends on humanís ability and not having accuracy. Each bone contains a thin material inside is known bone marrow. The components of erythrocytes and leucocytes and platelets. Basically Leukaemia is detected only by analysing the white blood cells. Focused only on WBC, Leukaemia Detection system analyses the microscopic image and conquer these Drawbacks. It extracts the necessary parts of images and direct applies some techniques. K-mean clustering is used only WBC (WHITE BLOOD CELL) detection. In this paper we describe a system for medical data processing that mainly uses ACO (Ant Colony Optimization) technique that provides the consequences for leukaemia detection and classification through multi-layer neural network (BPNN).
Keywords: Data Mining, ACO (Ant Colony Optimization, BPNN (Back Propagation Neural Network), K-mean clustering.