Efficient methods developed with deep learning in the last ten years have provided objectivity and high accuracy in the diagnosis of skin diseases. They also support accurate, cost-effective and timely treatment. In addition, they provide diagnoses without the need to touch patients, which is very desirable when the disease is contagious or the patients have another contagious disease. On the other hand, it is not possible to run deep networks on resource-constrained devices (e.g., mobile phones). Therefore, lightweight network architectures have been proposed in the literature. However, merely a few mobile applications have been developed for the diagnosis of skin diseases from colored photographs using lightweight networks. Moreover, only a few types of skin diseases have been addressed in those applications. Additionally, they do not perform as well as the deep network models, particularly for pattern recognition. Therefore, in this study, a novel model has been constructed using MobileNet. Also, a novel loss function has been developed and used. The main contributions of this study are: (i) proposing a novel hybrid loss function; (ii) proposing a modified-MobileNet architecture; (iii) designing and implementing a mobile phone application with the modified-MobileNet and a user-friendly interface. Results indicated that the proposed technique can diagnose skin diseases with 94.76% accuracy.

在过去的十年中，通过深度学习开发的有效方法为皮肤疾病的诊断提供了客观性和很高的准确性。他们还支持准确，具有成本效益和及时的治疗。此外，它们无需接触患者即可提供诊断，这在疾病具有传染性或患者患有另一种传染性疾病时非常理想。另一方面，不可能在资源受限的设备（例如，移动电话）上运行深度网络。因此，在文献中已经提出了轻量级的网络架构。然而，仅开发了一些移动应用程序以使用轻量级网络从彩色照片诊断皮肤疾病。此外，在那些应用中仅解决了几种类型的皮肤疾病。此外，它们的性能不如深度网络模型好，尤其是在模式识别方面。因此，在这项研究中，使用MobileNet构建了一个新颖的模型。而且，已经开发并使用了新颖的损失函数。这项研究的主要贡献是：（i）提出了一种新的混合损失函数；（ii）提出一种改进的MobileNet体系结构；（iii）使用修改后的MobileNet和用户友好的界面设计和实现手机应用程序。结果表明，该技术可以诊断皮肤疾病，准确率达94.76％。（i）提出一种新颖的混合损失函数；（ii）提出一种改进的MobileNet体系结构；（iii）使用修改后的MobileNet和用户友好的界面设计和实现手机应用程序。结果表明，该技术可以诊断皮肤疾病，准确率达94.76％。（i）提出一种新颖的混合损失函数；（ii）提出一种改进的MobileNet体系结构；（iii）使用修改后的MobileNet和用户友好的界面设计和实现手机应用程序。结果表明，该技术可以诊断皮肤疾病，准确率达94.76％。