用分割图像+CNN特征提升镰状细胞病分类效率
Improving Sickle Cell Disease Classification: A Fusion of Conventional Classifiers, Segmented Images, and Convolutional Neural Networks
- 融合传统分类器、图像分割与CNN特征进行分类
- 结合分割图像与SVM的准确率达96.80%
- 适合资源受限的医疗影像分析场景
镰状细胞贫血以红细胞形态异常为特征，可通过显微图像检测。医学计算技术能提升诊断与治疗效率。然而，许多基于卷积神经网络（CNN）的方法训练耗时且资源需求高，为低计算开销方法的研究提供了机会。本文提出一种新方法，结合传统分类器、图像分割与CNN，实现镰状细胞病的自动分类。我们评估了分割图像对分类的影响，深入探讨深度学习的集成方式。结果表明，使用分割图像和CNN特征配合支持向量机（SVM），分类准确率达到96.80%。该发现对计算资源有限的场景具有重要意义，为未来医疗影像分析研究提供新方向。
原文摘要 · Abstract (English)
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.
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