Mobile-based Intelligent Skin Diseases Diagnosis System

A. Sallam, Abdulfattah E. Ba Alawi
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引用次数: 5

Abstract

Skin diseases are the most common diseases in humans. The inherent variability in the appearance of skin diseases makes it hard even for medical experts to detect disease type from dermoscopic images. Recent advances in image processing using the Convolution Neural Networks have led to better results in diagnosing systems. We aim to develop an advanced diagnostic system in a manner that meets the requirements of real-time and extensibility of medical services for skin disease detection. The proposed system provides offline diagnosis for the users who have not Internet connection or online diagnosing uses on-cloud service. The user captures the affected area and get the offline immediate report. The schema offers the users a communication window with dermatologists to get medical recommendations in addition to an online accurate diagnosis service. The new images that are labeled by dermatologists are used to retrain the model to enhance model accuracy. To maximize the number of users, the system is implemented in a mobile-based environment. With the growing numbers of portable apps, it becomes easy for people to obtain up-to-date data. Users are familiar with looking for answers from the virtual globe including health issues. The following experimental results demonstrate the feasibility of the proposed method. The average obtained accuracy is 83% in testing cases.
基于移动的智能皮肤病诊断系统
皮肤病是人类最常见的疾病。皮肤病外观的内在可变性使得医学专家甚至很难从皮肤镜图像中检测疾病类型。使用卷积神经网络的图像处理的最新进展在诊断系统中取得了更好的结果。我们的目标是开发一个先进的诊断系统,以满足医疗服务的实时性和可扩展性的要求,为皮肤病检测。该系统为没有互联网连接的用户提供离线诊断或使用云服务进行在线诊断。用户捕获受影响的区域并获得离线即时报告。该模式为用户提供了一个与皮肤科医生沟通的窗口,以获得医疗建议以及在线准确诊断服务。由皮肤科医生标记的新图像用于重新训练模型,以提高模型的准确性。为了最大限度地增加用户数量,系统在基于移动的环境中实现。随着便携式应用程序数量的增加,人们获取最新数据变得很容易。用户熟悉从虚拟地球仪中寻找答案,包括健康问题。下面的实验结果证明了该方法的可行性。在测试用例中,平均获得的准确率为83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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