A Range-Threshold Based Medical Image Classification Algorithm for Crowdsourcing

• 作者: Haiwei Pan Shengnan Zhao Xiaoning Feng Xiaoqin Xie Zhiqiang Zhang
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: MEDICAL IMAGE Range-threshold BASED Crowdsourcing IMAGE CLASSIFICATION
• 年，卷（期）: 2017,（1）
• 页码范围: 110-112
• 页数: 3页
• 分类号: C5

摘要

Medical images are important for medical research and clinical diagnosis.The research of medical images includes image acquisition,processing,analysis and other related research fields.Crowdsourcing is attracting growing interests in recent years as an effective tool.It can harness human intelligence to solve problems that computers cannot perform well,such as sentiment analysis and image recognition.Crowdsourcing can achieve higher accuracies in medical image classification,but it cannot be widely used for its low efficiency and the monetary cost.We adopt a hybrid approach which combines computer’s algorithm and crowdsourcing system for image classification.Medical image classification algorithms have a high error rate near the threshold.And it is not significant by improving these classification algorithms to achieve a higher accuracy.To address the problem,we propose a hybrid framework,which can achieve a higher accuracy significantly than only use classification algorithms.At the same time,it only processes the images that classification algorithms perform not well,so it has a lower monetary cost.In the framework,we device an effective algorithm to generate a range-threshold that assign images to crowdsourcing or classification algorithm.Experimental results show that our method can improve the accuracy of medical images classification and reduce the crowdsourcing monetary cost.