A Crowdsourcingbased 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.

Evaluating the crowd quality for subjective questions based on a Spark computing environment Abstract   Crowdsourcing is a popular method of data collection or problem solving, which has been widely used in business, scientific research and other fields. Personnel quality control is a key issue in the field of crowdsourcing, and the method of estimating personnel quality is receiving more and more attention. This paper proposes a new algorithm for the quality estimation of crowdsourcing personnel, which is based on subjective questions innovatively. Specifically, confidence intervals are used to indicate personnel quality. To improve its scalability and efficiency in big data environment, algorithm is implemented on Spark, a widelyadopted distributed computing platform. Finally, extensive experiments are conducted on realworld data sets, and results demonstrate that algorithm can effectively and quickly estimate the quality of crowdsourcing personnel in the big data environment.