A Crowdsourcing-based Medical Image Classification Method

The main task of medical image mining is to effectively analyze medical image data. Medical image classification algorithms have a high error rate near the threshold. To address the problem, the paper adopts a hybrid approach which combines computers algorithm and crowdsourcing system for image classification. A hybrid framework is proposed, 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 this framework, a range threshold is generated by using an efficient algorithm that assigns an image to a 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. Due to the lack of the crowdsourcing platform for processing medical information, medical image classification results are difficult to collect, so this paper proposed a crowdsourcing platform for medical image classification.