Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks
Journal:
arXiv
Published Date:
Dec 23, 2024
Abstract
Sickle cell anemia, which is characterized by abnormal erythrocyte
morphology, can be detected using microscopic images. Computational techniques
in medicine enhance the diagnosis and treatment efficiency. However, many
computational techniques, particularly those based on Convolutional Neural
Networks (CNNs), require high resources and time for training, highlighting the
research opportunities in methods with low computational overhead. In this
paper, we propose a novel approach combining conventional classifiers,
segmented images, and CNNs for the automated classification of sickle cell
disease. We evaluated the impact of segmented images on classification,
providing insight into deep learning integration. Our results demonstrate that
using segmented images and CNN features with an SVM achieves an accuracy of
96.80%. This finding is relevant for computationally efficient scenarios,
paving the way for future research and advancements in medical-image analysis.
