Healthy and unhealthy red blood cell detection in human blood smears using neural networks.

One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classification of RBCs based on a microscopic examination in blood smears. This paper presents a proposed algorithm for detecting and counting three types of anaemia-infected red blood cells in a microscopic coloured image using circular Hough transform and morphological tools. Anaemia cells include sickle, elliptocytosis, microsite cells and cells with unknown shapes. Additionally, the resulting data from the detection process have been analysed by a prevalent data analysis technique: the neural network. The experimental results for this model have demonstrated high accuracy for analysing healthy/unhealthy cells. This algorithm has achieved a maximum detection of approximately 97.8% of all cells in 21 microscopic images. Effectiveness rates of 100%, 98%, 100%, and 99.3% have been achieved using neural networks for sickle cells, elliptocytosis cells, microsite cells and cells with unknown shapes, respectively.