论文标题
使用机器学习接近疟疾感染细胞的生物细胞分类，然后深度学习以比较和分析K-Nearest邻居和深度CNN
Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare & analyze K-Nearest Neighbours and Deep CNNs
论文作者
论文摘要
疟疾是一种致命的疾病，每年夺走数十万人的生命。通过提供有效的诊断成像和疾病鉴定分类方法，计算方法已被证明在医疗行业中很有用。本文在对细胞图像中疟疾的存在进行分类的背景下研究了不同的机器学习方法。许多机器学习方法可以应用于同一问题；一个机器学习方法是否更适合问题的问题在很大程度上依赖问题本身和模型的实现。特别是，卷积神经网络和K最近的邻居都与其应用有关疟疾的存在和每个模型的经验性能进行分析和对比。在这里，我们实施了两种分类模型。卷积神经网络和K最近的邻居算法。根据验证精度比较这两种算法。对于我们的实施，CNN（95％）的表现比KNN好25％（75％）。
Malaria is a deadly disease which claims the lives of hundreds of thousands of people every year. Computational methods have been proven to be useful in the medical industry by providing effective means of classification of diagnostic imaging and disease identification. This paper examines different machine learning methods in the context of classifying the presence of malaria in cell images. Numerous machine learning methods can be applied to the same problem; the question of whether one machine learning method is better suited to a problem relies heavily on the problem itself and the implementation of a model. In particular, convolutional neural networks and k nearest neighbours are both analyzed and contrasted in regards to their application to classifying the presence of malaria and each models empirical performance. Here, we implement two models of classification; a convolutional neural network, and the k nearest neighbours algorithm. These two algorithms are compared based on validation accuracy. For our implementation, CNN (95%) performed 25% better than kNN (75%).
