Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks

• Victor Júnio Alcântara Cardoso Universidade Federal de Viçosa
• Rodrigo Moreira Universidade Federal de Viçosa
• João Fernando Mari Universidade Federal de Viçosa
• Larissa Ferreira Rodrigues Moreira Universidade Federal de Viçosa / Universidade Federal de Uberlândia

DOI: https://doi.org/10.5753/eniac.2023.234076

Resumo

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.

Palavras-chave: sickle cell disease, deep learning, convolutional neural networks, conventional classifiers, classification, segmented images

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