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