Goal: Systemic sclerosis (SSc) is a rare autoimmune, systemic disease with prominent fibrosis of skin and internal organs. Early diagnosis of the disease is crucial for designing effective therapy and management plans. Machine learning algorithms, especially deep learning, have been found to be greatly useful in biology, medicine, healthcare, and biomedical applications, in the areas of medical image processing and speech recognition. However, the need for a large training data set and the requirement for a graphics processing unit (GPU) have hindered the wide application of machine learning algorithms as a diagnostic tool in resource-constrained environments (e.g., clinics). Methods: In this paper, we propose a novel mobile deep learning network for the characterization of SSc skin. The proposed network architecture consists of the UNet, a dense connectivity convolutional neural network (CNN) with added classifier layers that when combined with limited training data, yields better image segmentation and more accurate classification, and a mobile training module. In addition, to improve the computational efficiency and diagnostic accuracy, the highly efficient training model called “MobileNetV2,” which is designed for mobile and embedded applications, was used to train the network. Results: The proposed network was implemented using a standard laptop (2.5 GHz Intel Core i7). After fine tuning, our results showed the proposed network reached 100% accuracy on the training image set, 96.8% accuracy on the validation image set, and 95.2% on the testing image set. The training time was less than 5 hours. We also analyzed the same normal vs SSc skin image sets using the CNN using the same laptop. The CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. Additionally, it took more than 14 hours to train the CNN architecture. We also utilized the MobileNetV2 model to analyze an additional dataset of images and classified them as normal, early (mid and moderate) SSc or late (severe) SSc skin images. The network reached 100% accuracy on the training image set, 97.2% on the validation set, and 94.8% on the testing image set. Using the same normal, early and late phase SSc skin images, the CNN reached 100% accuracy on the training image set, 87.7% accuracy on the validation image set, and 82.9% on the testing image set. These results indicated that the MobileNetV2 architecture is more accurate and efficient compared to the CNN to classify normal, early and late phase SSc skin images. Conclusions: Our preliminary study, intended to show the efficacy of the proposed network architecture, holds promise in the characterization of SSc. We believe that the proposed network architecture could easily be implemented in a clinical setting, providing a simple, inexpensive, and accurate screening tool for SSc.

中文翻译：

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使用 MobileNetV2 模型对系统性硬化症皮肤进行深度学习分类

目标：系统性硬化症 (SSc) 是一种罕见的自身免疫性全身性疾病，具有明显的皮肤和内脏纤维化。疾病的早​​期诊断对于设计有效的治疗和管理计划至关重要。机器学习算法，尤其是深度学习，已被发现在医学图像处理和语音识别领域的生物学、医学、医疗保健和生物医学应用中非常有用。然而，对大型训练数据集的需求和对图形处理单元 (GPU) 的需求阻碍了机器学习算法作为诊断工具在资源有限的环境（例如诊所）中的广泛应用。方法：在本文中，我们提出了一种用于表征 SSc 皮肤的新型移动深度学习网络。所提出的网络架构由 UNet、一个带有附加分类器层的密集连接卷积神经网络 (CNN) 组成，当与有限的训练数据相结合时，可以产生更好的图像分割和更准确的分类，以及一个移动训练模块。此外，为了提高计算效率和诊断准确性，我们使用了专为移动和嵌入式应用设计的高效训练模型“MobileNetV2”来训练网络。结果：建议的网络是使用标准笔记本电脑（2.5 GHz Intel Core i7）实现的。经过微调，我们的结果表明，所提出的网络在训练图像集上达到了 100% 的准确率，在验证图像集上达到了 96.8% 的准确率，在测试图像集上达到了 95.2%。培训时间不到5小时。我们还使用同一台笔记本电脑使用 CNN 分析了相同的正常与 SSc 皮肤图像集。CNN 在训练图像集上达到 100% 的准确率，在验证图像集上达到 87.7% 的准确率，在测试图像集上达到 82.9%。此外，训练 CNN 架构需要 14 个多小时。我们还利用 MobileNetV2 模型分析了一个额外的图像数据集，并将它们分类为正常、早期（中度和中度）SSc 或晚期（严重）SSc 皮肤图像。网络在训练图像集上达到 100% 的准确率，97。验证集上为 2%，测试图像集上为 94.8%。使用相同的正常、早期和晚期 SSc 皮肤图像，CNN 在训练图像集上达到 100% 的准确度，在验证图像集上达到 87.7% 的准确度，在测试图像集上达到 82.9%。这些结果表明，与 CNN 相比，MobileNetV2 架构在对正常、早期和晚期 SSc 皮肤图像进行分类方面更加准确和高效。结论：我们的初步研究旨在展示所提出的网络架构的有效性，在 SSc 的表征中具有前景。我们相信，所提出的网络架构可以很容易地在临床环境中实施，为 SSc 提供简单、廉价和准确的筛查工具。