Online ISSN: 2515-8260

Keywords : Sickle cell anemia


INTEGRATION OF MULTI-MODAL FEATURES FOR SICKLE CELL ANEMIA IDENTIFICATION USING MULTILAYER PERCEPTRON

G. Sridevi; V. Shanthi; J. Josphin Mary; R. Charanya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1407-1412
DOI: 10.31838/ejmcm.07.09.146

SCA is a legacy community of diseases of red blood cells. Persons with sickle cells in their red blood cells have abnormal protein. Sickle cell anemia is a red cell condition that is inherited and does not produce enough rotary cells in the body to hold oxygen. It may cause severe pain, anaemia and other symptoms. It is dangerous. The early diagnosis is required for sickle cell anemia. In this study, the integration of multi-modal features for sickle cell anemia identification using multilayer perceptron of SCA system is discussed. Initially, the input images are given to multimodel feature is used for feature extraction and Multilayer Perceptron (MP) classifier is used for classification. The performance of SCA system produces the classification accuracy of 95%using MP classifier.

AUTOMATIC CLASSIFICATION OF SICKLE CELL ANEMIA USING RANDOM FOREST CLASSIFIER

S. Ranjana; R. Manimegala; K. Priya

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1530-1534
DOI: 10.31838/ejmcm.07.09.165

SCA is a genetic category of red diseases of the blood cells. People in their red blood cells contain an abnormal protein. Part of a group of SCA diseases is sickle cell anaemia (SCA). Sickle cell anaemia is a red blood cell condition that has not been inherited in the body with ample red cells to hold oxygen.. It is dangerous because it can cause extreme pain, anemia and other symptoms. The early diagnosis is required for sickle cell anemia. In this study, the automatic classification of SCA system is discussed. Initially, the input images are given to median filter for pre-processing. Then the Gray Level Co-occurrence Matrix (GLCM) and Haralick features are extracted. Finally, Random Forest (RF) classifier is used for Prediction. The performance of SCA system produces the classification accuracy of 95%using RF classifier.