COMPARISON OF DEEP LEARNING ALEXNET AND SUPPORT VECTOR MACHINE TO CLASSIFY SEVERITY OF SICKLE CELL ANEMIA

Abstract

Sickle cell anemia (SCA) is a serious hematological blood disorder, where affected patients are
frequently hospitalized throughout a lifetime. Most of the patient's life span reduced, and some
become addict based on the nature of strong analgesic that is taken by the concern patients,
which they all have strong side effects. The existing method of severity classification for SCA
patient is done manually through a microscope which is time-consuming, tedious, prone to error,
and require a trained hematologist. The affected patient has many cell shapes that show
important biomechanical characteristics of patient severity level. The main purpose of the study
is to develop an automated severity level classification method of SCA patients by comparing
deep learning AlexNet and Support Vector Machine (SVM) to enable present the percentage of
each cell present in blood smear image. Hence, having an effective way of classifying the
abnormalities present in the SCA disease based on the level of patient severity to give a better
insight into managing the concerned patient's life. The study was performed with 182 SCA
patients (over 11,000 single RBC images) with 14 classes of abnormalities and a class of normal
cells to develop a shape factor quantification and general multiscale shape analysis to classify the
patient based on severity level. As a result, it was found that the proposed framework can detect
85.4% abnormalities in SCA patient blood smear in automated manner when compared with
Support Vector Machine (SVM) method with 71.9%. Hence, the system classifies the severity of
SCA patient automatically and reduce the time and eye stress with performance AlexNet model
performance of 95.1% accuracy, 99.1% specificity, and 98.5% precision value.