Machine Learning Techniques for Automatic Detection of Sickle Cell Anemia using Adaptive Thresholding and Contour-based Segmentation Method

Automatic diagnosis of diseases in the medical field using image processing techniques has evolved tremendously recent times. Sickle cell anemia (SCA) is a kind disease connected with red blood cells (RBCs) present human body which deformation take place. The purpose this work to propose an automatic technique for detection from microscopic images. This paper mainly focuses on SCA novel segmentation method encompassing local adaptive thresholding and active contour-based algorithm. For sickle cells, supervised classifiers such as Artificial Neural Network (ANN) Support Vector Machine (SVM) are used. Here, geometric features healthy unhealthy RBCs calculated applied these classifiers. In approach, performance found slightly greater SVM classifier than ANN trained scaled conjugate gradient back-propagation (BP) algorithm hidden layer ten neurons. proposed approach achieves maximum 99.2% accuracy classifier. also studied seven different training algorithms by varying numbers Comparative analysis performances shows that, resilient BP 10 neurons gave moderately better 99% accuracy. contour efficient classification patients SCA.