基于手机的卷积神经网络皮肤病诊断系统(CNN)

Mwp Maduranga, D. Nandasena
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引用次数: 8

摘要

. 皮肤癌对全世界每个人都是一个严重的危害。然而,很难做出准确的皮肤癌诊断。深度学习算法最近在几个不同的任务中表现出色。它们也主要用于皮肤病的诊断工作。建议的技术在HAM10000数据集上的准确率约为85%,优于现有的方法。它在检测受影响区域方面的弹性相当快,比标准MobileNet模型的计算量减少了近2倍,从而降低了计算工作量。另一方面,移动应用程序是为快速和准确的操作而构建的。通过观察患处在皮肤病初期的图像,它可以帮助患者和皮肤科医生确定目前的疾病类型。根据这些发现,建议的方法可以帮助全科医生快速准确地诊断皮肤病,从而避免未来的并发症和死亡率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mobile-Based Skin Disease Diagnosis System Using Convolutional Neural Networks (CNN)
. Skin cancer is a serious hazard to everyone throughout the world. However, it is difficult to make an accurate skin cancer diagnosis. Deep learning algorithms have recently excelled in several different tasks. They've also been used for skin disease diagnosis jobs mainly. With around 85% accuracy, the suggested technique outperforms existing methods on the HAM10000 dataset. Its resilience in detecting the impacted region considerably faster with nearly 2x fewer computations than the standard MobileNet model results in low computing efforts. A mobile application, on the other hand, is built for quick and accurate action. By looking at an image of the afflicted area at the beginning of a skin illness, it assists patients and dermatologists in determining the kind of disease present. According to these findings, the suggested approach can assist general practitioners in quickly and accurately diagnosing skin diseases, therefore avoiding future complications and mortality.
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