Abstract
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.
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Acknowledgement
The paper is partly supported by the National Natural Science Foundation of China under Grant Nos. 61672181, 61370084, 61272184 and 61202090, Natural Science Foundation of Heilongjiang Province under Grant No. F2016005.
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Zhao, S., Pan, H., Xie, X., Zhang, Z., Feng, X. (2017). A Range-Threshold Based Medical Image Classification Algorithm for Crowdsourcing Platform. In: Zou, B., Li, M., Wang, H., Song, X., Xie, W., Lu, Z. (eds) Data Science. ICPCSEE 2017. Communications in Computer and Information Science, vol 727. Springer, Singapore. https://doi.org/10.1007/978-981-10-6385-5_37
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DOI: https://doi.org/10.1007/978-981-10-6385-5_37
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