Abstract
Anaemia disease attacks and deforms the circular red blood cells. Latterly, it has classified as a very dangerous disease. Many papers have been presented approaches for tracking and detection of anaemia cells before; but this time, elliptocytosis, sickle, and burr cells have detected based on their shape signatures. In tested images, some of the cells have been formed unknown shapes resulted from stuck operation in the instant image capture. This shape have not been belonged to any of anaemia kinds, then they have considered as cells with unknown shape. Here, the using of Circular Hough transforms, watershed segmentation and some of the morphological methods has been urgent manner to enhancing and preparing tested images. The performance of proposed algorithm have been achieved highly accuracy by testing 45 colourful microscopic images in 15 samples from patients already have anaemia disease. The Support Vector Machine (SVM), back propagation (BP) and self-organising map (SOM) neural networks have been applied on all information data of mentioned kinds of anaemia.