Iron deficiency Anemia disease is caused by the lack of iron in the human body. Iron helps the construction of Hemoglobin in the body. Consequently, the shortage of this material causes the shortage of Hemoglobin protein in the blood. By the reduction of Hemoglobin in the blood, the number of red blood cells will decrease and the size of these cells will become smaller than the normal blood cells. Also, the general shape of the red blood cells will change (Poikilocyte cells). Routine medical procedure that deals with the diagnosis of iron deficiency anemia is based on the complete blood cell count (CBC), a method which is quite time consuming. In this thesis, a fully automated algorithm is suggested for the diagnosis of iron deficiency anemia. The recommended approach has appropriate accuracy and precision and it also does not require sophisticated equipments. The algorithm is based on the difference between the number of Poikilocyte cells in normal and Iron deficient blood samples. The automatic detection procedure contains 6 steps: 1) collecting database 2) Preprocessing methods 3) Binarization and Segmentation 4) Feature Extraction 5) justify; MARGIN: 0in 0in 0pt; unicode-bidi: embed; DIRECTION: ltr" dir=ltr Keywords: Iron deficiency, Poikilocyte cell, Support Vector Machine, Neural Network, Decision Tree, K-nearest neighbor
