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Detecting distorted and
benign blood cells using the
Hough transform based on
neural networks and decision
Hany A. Elsalamony Mathematics Department, Faculty of Science, Helwan University, Cairo, Egypt
Sickle-cell anemia is one of the most important types of anemia. This paper presents an algorithm for
detecting blood cells characteristic of sickle-cell anemia. First, I discuss the construction of an algorithm
that can be used to detect and count benign or distorted red blood cells (RBCs) in a microscopic colored
image, even if those cells are hidden or overlapped. Second, I explain the process for checking and ana-
lyzing the constructed RBC data by applying two important techniques in data mining: the neural net-
work (NN) and the decision tree. I then review experiments demonstrating that these models show high
accuracy when predicting the counts of benign or distorted cells. In these experiments, the algorithm has
segmented around 99.98% of all input cells, helping to improve the diagnosis of sickle-cell anemia. The
NN has shown a 96.9% agreement with the algorithm's prediction outcomes, and the classification and
regression tree has achieved 92.9%.
Sickle-cell anemia
Image watershed segmentation
Red blood cell detection and counting
C&R tree
Neural network
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