首页> 外文会议>International Conference on Smart Systems and Inventive Technology >Real Time Application of Deep Learning Approach in Exogenous Skin Problem Identification
【24h】

Real Time Application of Deep Learning Approach in Exogenous Skin Problem Identification

机译:深度学习方法在外源性皮肤问题识别中的实时应用

获取原文

摘要

The proposed research work provides an easy way to the dermatologists and other medical professionals to acknowledge any given skin problem in a better way. The proposed work based on deep learning algorithm aims to make up the need of dermatologists in India. In the landmass sized country roughly 11,000 derma experts are employed, which means less than one derma experts for 100,000 patients. This work aims to help relieve the circumstances. It can diagnose five types of skin problems like redness. swelling. puss. cold sore. and tinea versicolor. The proposed system assure the patients with an option to self-check their signs or symptoms like redness swelling etc that makes this work a real time application. The patient can also then associate to a dermatologist online for consultation within a momentary span of time by unloading their photos to hospital website, and dermatologists give guidance by analyzing the photos with faster R-CNN deep learning algorithm. The system uses tensorflow object detection framework with faster RCNN configuration for skin problem like redness, pus and swelling assessment and successfully estimates these skin problem depending on maximum scores from the trained model. The main aim of this system is to achieve maximum accuracy of dermatological problem prediction.
机译:拟议的研究工作为皮肤科医生和其他医学专业人士提供了一种简便的方法,可以更好地确认任何给定的皮肤问题。基于深度学习算法的拟议工作旨在弥补印度皮肤科医生的需求。在陆地面积大的国家,大约有11,000名皮肤病专家受雇,这意味着100,000名患者的皮肤病专家少于一名。这项工作旨在帮助缓解这种情况。它可以诊断五种类型的皮肤问题,例如发红。肿胀。猫。唇疱疹。和花斑癣。所提出的系统向患者保证可以自我检查其体征或症状,例如发红肿胀等,从而使这项工作成为实时应用。然后,患者还可以通过将他们的照片卸载到医院网站上,在短暂的时间范围内与皮肤科医生在线联系以进行咨询,皮肤科医生可以通过使用更快的R-CNN深度学习算法分析照片来提供指导。该系统使用具有更快的RCNN配置的tensorflow对象检测框架来解决诸如发红,脓液和肿胀评估之类的皮肤问题,并根据来自训练模型的最高评分成功估算出这些皮肤问题。该系统的主要目的是实现皮肤病学问题预测的最大准确性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号