Machine Learning: Sickle Cell Disease Image Analysis

Abstract

Sickle cell Disease is a blood disorder that resulting from the abnormalities happen in Red blood cells (RBCs). Patients suffering from this disease experience acute pain and infection that will lead to the chronic conditions and damage of organ. This research paper investigates the method and techniques to classify the normal RBCs and abnormal RBCs Sickle cell. This paper discussed about Artificial Neural Networks as chosen algorithm form Machine Learning to perform fast and accurate classification of blood cells. The objectives of this paper is to study the effectiveness of Machine Learning by applying ANN algorithm can be used to classify the blood cells, to apply ANN algorithm in MATLAB Software for counting and classification of normal RBCs and Sickle cells and to provide new method in sickle cell counting classification in inexpensive way and faster. The purposed of this research paper is to help in diagnosing Sickle Cell Anemia disease. The proposed method is to replace the existing method in hospital which the blood cell is counting by laboratory task using device called Hemocytometer attached to microscope. The blood smear is view through microscope and the physician will count the cells using hand tally counter. The existing method consumes longer time and require skills to recognize the complex shape of sickle cells. This research will discuss about a few steps on image processing and classification process by ANN. Image processing steps proposed in this study includes pre-processing, image enhancement and image segmentation.