A Medical Image Classification Algorithm For Crowdsourcing Platform

The popularity of medical images and computers has prompted the rapid development of medical informatization and the surge of data volume of various types of medical images.At present,the main task of medical image data mining is to effectively analyze medical image data information.So,we can find valuable information and knowledge to help doctors.Crowdsourcing can achieve higher accuracy 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.To ensure the quality of crowdsourcing answers,this paper presents two worker models,Worker Quality Evaluation Model(WQEM)and Worker Performance Prediction Model(WPPM)respectively.Given the lack of the crowdsourcing platform in deal with medical information,medical image classification result is difficult to collect,so this paper designed and implemented a crowdsourcing platform for medical image classification of medical image classification to complete the follow-up work.Experimental results show that our method can improve the accuracy of medical images classification and reduce the crowdsourcing monetary cost.