Medical image classification plays an essential role in clinical treatment and teaching tasks. However, the traditional method has reached its ceiling on performance. Moreover, by using them, much time and effort need to be spent on extracting and selecting classification features. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Therefore, this paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. Three techniques are evaluated through experiments. These are linear support vector machine classifier with local rotation and orientation free features, transfer learning on two convolutional neural network models: Visual Geometry Group i.e., VGG16 and InceptionV3, and a capsule network training from scratch. Data augmentation is a data preprocessing method applied to all three methods. The results of the experiments show that data augmentation generally is an effective way for all three algorithms to improve performance. Also, Transfer learning is a more useful classification method on a small dataset compared to a support vector machine with oriented fast and rotated binary (ORB) robust independent elementary features and capsule network. In transfer learning, retraining specific features on a new target dataset is essential to improve performance. And, the second important factor is a proper network complexity that matches the scale of the dataset.

中文翻译：

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基于深度卷积神经网络的医学图像分类用于疾病诊断

医学图像分类在临床治疗和教学任务中起着至关重要的作用。但是，传统方法已达到性能极限。此外，通过使用它们，需要花费大量时间和精力来提取和选择分类特征。深度神经网络是一种新兴的机器学习方法，已证明其在不同分类任务中的潜力。值得注意的是，在各种图像分类任务中，卷积神经网络以最佳结果为主导。但是，很难收集医学图像数据集，因为它需要大量专业知识才能对其进行标记。因此，本文研究如何在胸部X射线数据集上应用基于卷积神经网络（CNN）的算法对肺炎进行分类。通过实验评估了三种技术。这些是具有局部旋转和无方向特征的线性支持向量机分类器，在两个卷积神经网络模型上转移学习：视觉几何组，即VGG16和InceptionV3，以及从头开始训练的胶囊网络。数据扩充是一种应用于所有三种方法的数据预处理方法。实验结果表明，数据增强通常是所有三种算法提高性能的有效方法。此外，与具有定向快速旋转二进制（ORB）鲁棒独立基本特征和胶囊网络的支持向量机相比，转移学习在小型数据集上是一种更有用的分类方法。在转移学习中，重新训练新目标数据集上的特定功能对于提高性能至关重要。和，