Identification of Sickle Cell Anemia Using Deep Neural Networks

A molecule called hemoglobin is found in red blood cells that holds oxygen all over the body. Hemoglobin elastic, round, and stable a healthy human. This makes it possible to float across cells. But composition of unhealthy if you have sickle cell disease. It refers compact bent The odd obstruct flow blood. dangerous can result severe discomfort, organ damage, heart strokes, other symptoms. human life expectancy be shortened as well. early identification calls will help people recognize signs assist antibiotics, supplements, transfusion, pain-relieving medications, treatments etc. manual assessment, diagnosis, count are time consuming process may misclassification since millions one spell. When utilizing data mining techniques such multilayer perceptron classifier algorithm, effectively detected with high precision proposed approach tackles limitations research by implementing powerful efficient MLP (Multi-Layer Perceptron) classification algorithm distinguishes Sickle Cell Anemia (SCA) into three classes: Normal (N), Cells(S) Thalassemia (T) paper also presents degree popular machine learning algorithms on dataset obtained from Society (TSCS) located Rajendra Nagar, Hyderabad, Telangana, India. Doi: 10.28991/esj-2021-01270 Full Text: PDF