Title: Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models Show Chinese title

Title: 使用CNN和目标检测模型的计算机辅助临床皮肤疾病诊断

Abstract: Skin disease is one of the most common types of human diseases, which may happen to everyone regardless of age, gender or race. Due to the high visual diversity, human diagnosis highly relies on personal experience; and there is a serious shortage of experienced dermatologists in many countries. To alleviate this problem, computer-aided diagnosis with state-of-the-art (SOTA) machine learning techniques would be a promising solution. In this paper, we aim at understanding the performance of convolutional neural network (CNN) based approaches. We first build two versions of skin disease datasets from Internet images: (a) Skin-10, which contains 10 common classes of skin disease with a total of 10,218 images; (b) Skin-100, which is a larger dataset that consists of 19,807 images of 100 skin disease classes. Based on these datasets, we benchmark several SOTA CNN models and show that the accuracy of skin-100 is much lower than the accuracy of skin-10. We then implement an ensemble method based on several CNN models and achieve the best accuracy of 79.01\% for Skin-10 and 53.54\% for Skin-100. We also present an object detection based approach by introducing bounding boxes into the Skin-10 dataset. Our results show that object detection can help improve the accuracy of some skin disease classes. Show Chinese abstract

Abstract: 皮肤疾病是人类最常见的疾病类型之一，可能发生在任何人身上，无论年龄、性别或种族。由于视觉多样性高，人类诊断高度依赖个人经验；并且在许多国家，有经验的皮肤科医生严重短缺。为了缓解这个问题，使用最先进的（SOTA）机器学习技术的计算机辅助诊断将是一个有前景的解决方案。在本文中，我们旨在了解基于卷积神经网络（CNN）的方法的性能。我们首先从互联网图像中构建了两种皮肤疾病数据集：（a）Skin-10，其中包含10种常见的皮肤疾病，共有10,218张图像；（b）Skin-100，这是一个更大的数据集，由100种皮肤疾病类别的19,807张图像组成。基于这些数据集，我们对几种SOTA CNN模型进行了基准测试，并显示Skin-100的准确率远低于Skin-10的准确率。然后，我们实现了一种基于多个CNN模型的集成方法，并在Skin-10上达到了最佳准确率79.01%，在Skin-100上达到了53.54%。我们还通过在Skin-10数据集中引入边界框，提出了一种基于目标检测的方法。我们的结果表明，目标检测可以帮助提高某些皮肤疾病类别的准确率。