基于XceptionNet的血细胞图像疟疾检测
Malaria Detection from Blood Cell Images Using XceptionNet
AI总结：
本文利用XceptionNet等深度卷积网络从血细胞图像中自动提取特征并分类，在公开疟疾数据集上达到97.55%的平均准确率，实现可靠检测并减少人工参与。
摘要：
疟疾主要通过雌性按蚊叮咬传播，常导致人员死亡，尤其是0-5岁年龄段的儿童。临床专家通过显微镜观察血液涂片图像中的红细胞来识别疟疾。缺乏足够的专业知识和技能，以及最重要的手动参与，可能导致错误诊断。因此，计算机辅助的自动诊断成为首选的替代方案。本文应用了经过充分验证的深度网络从血细胞图像中提取深层内在特征，并将其分类为疟疾感染细胞或健康细胞。本研究采用了六种深度卷积网络，即AlexNet、XceptionNet、VGG-19、残差注意力网络、DenseNet-121和自定义CNN。在公开的疟疾细胞图像数据集上，残差注意力网络和XceptionNet的表现相对优于其他网络。它们分别取得了97.28%和97.55%的平均准确率，超过了同一数据集上的其他相关方法。这些发现极大地鼓舞了基于深度学习的自动可靠疟疾检测方法的现实应用，同时最大限度地减少了直接人工参与。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
