用深度学习自动识别疟疾血细胞，准确率达97.55%。
Malaria Detection from Blood Cell Images Using XceptionNet
- 基于XceptionNet提取血细胞图像深层特征
- 在公开数据集上达到97.55%分类准确率
- 适合医疗辅助诊断与基层筛查应用
疟疾主要通过雌性按蚊叮咬传播，常导致儿童（0-5岁）死亡。临床专家通过显微镜观察涂片中的红细胞判断是否感染。专业人才不足及人工操作易导致误诊。为此，本文采用六种深度卷积网络（AlexNet、XceptionNet、VGG-19、残差注意力网络、DenseNet-121和自定义CNN）从血细胞图像中提取深层特征并分类为感染或健康细胞。实验表明，残差注意力网络与XceptionNet表现最优，平均准确率分别达97.28%和97.55%，优于同类方法。结果证明深度学习可用于实现高效可靠的疟疾自动检测，减少人工干预。
原文摘要 · Abstract (English)
Malaria, which primarily spreads with the bite of female anopheles mosquitos, often leads to death of people - specifically children in the age-group of 0-5 years. Clinical experts identify malaria by observing RBCs in blood smeared images with a microscope. Lack of adequate professional knowledge and skills, and most importantly manual involvement may cause incorrect diagnosis. Therefore, computer aided automatic diagnosis stands as a preferred substitute. In this paper, well-demonstrated deep networks have been applied to extract deep intrinsic features from blood cell images and thereafter classify them as malaria infected or healthy cells. Among the six deep convolutional networks employed in this work viz. AlexNet, XceptionNet, VGG-19, Residual Attention Network, DenseNet-121 and Custom-CNN. Residual Attention Network and XceptionNet perform relatively better than the rest on a publicly available malaria cell image dataset. They yield an average accuracy of 97.28% and 97.55% respectively, that surpasses other related methods on the same dataset. These findings highly encourage the reality of deep learning driven method for automatic and reliable detection of malaria while minimizing direct manual involvement.
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