论文标题
使用浅卷积神经网络从RBC图像中检测到疟疾
Malaria detection from RBC images using shallow Convolutional Neural Networks
论文作者
论文摘要
VGG-16和Resnet-50（Resnet-50）等深度学习模型的出现已经大大彻底改变了图像分类领域，并且通过使用这些卷积神经网络（CNN）体系结构，可以在各种图像数据集中获得高分类的精度。但是，这些深度学习模型具有很高的计算复杂性，因此会产生运行这些算法的高计算成本，并且很难解释结果。在本文中，我们提出了一个浅CNN结构，该结构具有与VGG-16和Resnet-50模型相同的分类准确性，用于稀薄的血液涂片RBC幻灯片图像，以检测疟疾，同时通过数量级减少计算运行时间。这可以为这些算法的商业部署提供重要优势，尤其是在非洲较贫穷的国家以及印度次大陆的某些地区，那里的疟疾威胁很严重。
The advent of Deep Learning models like VGG-16 and Resnet-50 has considerably revolutionized the field of image classification, and by using these Convolutional Neural Networks (CNN) architectures, one can get a high classification accuracy on a wide variety of image datasets. However, these Deep Learning models have a very high computational complexity and so incur a high computational cost of running these algorithms as well as make it hard to interpret the results. In this paper, we present a shallow CNN architecture which gives the same classification accuracy as the VGG-16 and Resnet-50 models for thin blood smear RBC slide images for detection of malaria, while decreasing the computational run time by an order of magnitude. This can offer a significant advantage for commercial deployment of these algorithms, especially in poorer countries in Africa and some parts of the Indian subcontinent, where the menace of malaria is quite severe.
